Method and system for producing formatted data related to geometric distortions

ABSTRACT

The invention relates to a processing system and method for producing formatted information related to the appliances (APP 1  to  3 ) of an appliance chain (APP 1 ). This appliance chain comprises in particular at least one image-capture appliance (APP 1 ) and/or at least one image-restitution appliance (App 3 ) for capturing and/or restituting an image (M) on a medium (SC). The invention aims at producing formatted information related to the geometric distortions of at least one appliance of the said chain. The invention provides different alternative embodiments, especially that with which fixed characteristics of the appliances and/or variable characteristics depending on the image (I) can be taken into account. The fixed characteristics and/or the variable characteristics are capable of being associated with one or more characteristic values, especially the focal length and/or the focusing. The invention then provides for producing measured formatted information related to the geometric distortions of the said appliance from a measured field D(H). The invention is applicable to photographic or video image processing, in optical devices, industrial controls, robotics, metrology, etc.

PREAMBLE OF THE DESCRIPTION FIELD IN QUESTION, PROBLEM POSED

[0001] The present invention relates to a method and a system forproducing formatted information related to geometric distortions.

Solution Method

[0002] The invention relates to a method for producing formattedinformation related to the appliances of an appliance chain. Theappliance chain includes in particular at least one image-captureappliance and/or at least one image-restitution appliance. The methodincludes the stage of producing formatted information related to thegeometric distortions of at least one appliance of the chain.

[0003] Preferably, according to the invention, the appliance is capableof capturing or restituting an image on a medium. The appliance containsat least one fixed characteristic and/or one variable characteristicdepending on the image. The fixed characteristic and/or variablecharacteristic can be associated with one or more values ofcharacteristics, especially the focal length and/or the focusing andtheir values of associated characteristics. The method includes thestage of producing, from a measured field, measured formattedinformation related to the geometric distortions of the appliance. Theformatted information may include the measured formatted information.

Extended Formatted Information and Deviation

[0004] Preferably, according to the invention, the method additionallyincludes the stage of producing extended formatted information relatedto the geometric distortions of the appliance from measured formattedinformation. The formatted information can include the extendedformatted information. The extended formatted information exhibits adeviation compared with the said measured formatted information.

[0005] Preferably, according to the invention, the method is such thatthe formatted information produced from the measured formattedinformation is represented by the parameters of a parameterizable modelchosen from among a set of parameterizable models, especially a set ofpolynomials. The method additionally includes the stage of selecting theparameterizable model within the set of parameterizable models by:

[0006] defining a maximum deviation,

[0007] ordering the parameterizable models of the set of parameterizablemodels in accordance with their degree of complexity of employment,

[0008] choosing the first of the parameterizable models of the orderedset of parameterizable models in such a way that the deviation issmaller than the maximum deviation.

[0009] According to an alternative embodiment of the invention, theextended formatted information may be the measured formattedinformation.

[0010] Preferably, according to the invention, the method includes afirst calculation algorithm with which the measured field can beobtained from a universal set containing characteristic points and froma virtual reference composed of reference points on a reference surface.The first calculation algorithm includes the stage of capturing or ofrestituting the universal set by means of the appliance to produce animage of characteristic points on the medium. The image of acharacteristic point is defined hereinafter as the characteristic imagepoint.

[0011] The first calculation algorithm additionally includes:

[0012] the stage of establishing a bijection between the characteristicimage points and the reference points,

[0013] the stage of selecting zero or one or more variablecharacteristics, referred to hereinafter as selected variablecharacteristics, among the set of variable characteristics.

[0014] The measured field is composed of:

[0015] the set of pairs composed of one of the reference points and ofthe characteristic image point associated by the bijection, and of

[0016] the value, for the image in question, of each of the selectedvariable characteristics.

[0017] Preferably, according to the invention, the method additionallyincludes the stage of choosing a mathematical projection, especially abilinear transformation, between the medium and the reference surface.The measured field is composed of the value, for the image, of each ofthe selected variable characteristics and, for each reference point:

[0018] of the pair composed of the reference point and of themathematical projection, onto the reference surface, of thecharacteristic image point associated by the bijection with thereference point, and/or

[0019] of the pair composed of the characteristic image point associatedby the bijection with the reference point, and of the mathematicalprojection of the reference point onto the medium.

Interpolation to Format an Arbitrary Point

[0020] Preferably, according to the invention, the method additionallyincludes the stage of obtaining, from measured formatted information,the extended formatted information related to an arbitrary referencepoint on the reference surface and/or related to an arbitrarycharacteristic image point of the medium, by deducing the formattedinformation related to the arbitrary reference point or to the arbitrarycharacteristic image point.

Variable Focal Length

[0021] Preferably, according to the invention, the method is such thatthe appliance of the appliance chain is provided with at least onevariable characteristic depending on the image, especially the focallength and/or the focusing. Each variable characteristic is capable ofbeing associated with a value to form a combination composed of the setof variable characteristics and values. The method additionally includesthe following stages:

[0022] the stage of selecting predetermined combinations,

[0023] the stage of calculating measured formatted information,especially by employing the first calculation algorithm for each of thepredetermined combinations selected in this way.

Variable Focal Length—Formatting at an Arbitrary Point

[0024] An argument is defined, depending on the case, as:

[0025] an arbitrary reference point on the reference surface and acombination, or

[0026] an arbitrary characteristic image point of the medium and acombination.

[0027] Preferably, according to the invention, the method additionallyincludes the stage of deducing, from measured formatted information, theextended formatted information related to an arbitrary argument. Itresults from the combination of technical features that the formattedinformation is more compact and resistant to measurement errors.

Choice of a Threshold for the Deviation, and Formatting According tothis Threshold

[0028] Preferably, according to the invention, the method is such that,in order to deduce the extended formatted information from measuredformatted information:

[0029] a first threshold is defined,

[0030] the extended formatted information is selected in such a way thatthe deviation is below the first threshold.

Addition of the Deviations to the Formatted Information

[0031] Preferably, according to the invention, the method additionallyincludes the stage of associating the deviations with the formattedinformation. It results from the combination of technical features thatthe formatted information can be used by the software for processingimages captured by the appliance in order to obtain images whoseresidual geometric distortion is known. It results from the combinationof technical features that the formatted information can be used byimage-processing software to obtain images intended to be restituted byan image-restitution appliance with known residual geometric distortion.

Choice of Bilinear Transformation

[0032] Preferably, according to the invention, the method additionallyincludes the stage of selecting, on the medium, four characteristicimage points such that the quadrilateral defined by the fourcharacteristic image points is that having a maximum area and a centerof gravity situated in the proximity of the geometric center of theimage. The mathematical projection is the bilinear transformation thattransforms the four characteristic image points to the reference pointsassociated by bijection with the four characteristic image points. Itresults from the combination of technical features that it is thenpossible in simple manner to obtain formatted information that can beused by image-processing software to capture or restitute images with asmall change of perspective.

Case of Color Image Distortions

[0033] Preferably, according to the invention, the image is a colorimage composed of a plurality of color planes. The method additionallyincludes the stage of producing the measured formatted information byemploying the first calculation algorithm for at least two of the colorplanes, by using the same mathematical projection for each of the colorplanes. In this way it is possible to use the formatted informationand/or measured formatted information to correct the distortions and/orthe chromatic aberrations of the appliance.

[0034] Preferably, according to the invention, the image is a colorimage composed of a plurality of color planes. The method additionallyincludes the stage of producing the measured formatted information byemploying the first calculation algorithm for at least one of the colorplanes, by using the same virtual reference for each of the colorplanes. In this way it is possible to use the formatted informationand/or measured formatted information to correct the chromaticaberrations of the appliance.

System

[0035] The invention relates to a system for producing formattedinformation related to the appliances of an appliance chain. Theappliance chain includes in particular at least one image-captureappliance and/or at least one image-restitution appliance. The systemincludes calculating means for producing formatted information relatedto the geometric distortions of at least one appliance of the chain.

[0036] The appliance is capable of capturing or restituting an image ona medium. The appliance contains at least one fixed characteristicand/or one variable characteristic depending on the image. The fixedcharacteristic and/or variable characteristic can be associated with oneor more values of characteristics, especially the focal length and/orthe focusing and their values of associated characteristics. Preferably,according to the invention, the system includes calculating means forproducing, from a measured field, measured formatted information relatedto the geometric distortions of the appliance. The formatted informationmay include the measured formatted information.

Extended Formatted Information and Deviation

[0037] Preferably, according to the invention, the system additionallyincludes calculating means for producing extended formatted informationrelated to the geometric distortions of the appliance from measuredformatted information. The formatted information can include theextended formatted information. The extended formatted informationexhibits a deviation compared with the measured formatted information.

Concept of Model—Interpolation—Choice of a Threshold and Choice of theSimplest Model for Arriving at the Threshold

[0038] Preferably, according to the invention, the system is such thatthe formatted information produced from the measured formattedinformation is represented by the parameters of a parameterizable modelchosen from among a set of parameterizable models, especially a set ofpolynomials. The system additionally includes selection means forselecting the parameterizable model within the set of parameterizablemodels. The selection means include data-processing means for:

[0039] defining a maximum deviation,

[0040] ordering the parameterizable models of the set of parameterizablemodels in accordance with their degree of complexity of employment,

[0041] choosing the first of the parameterizable models of the orderedset of parameterizable models in such a way that the deviation issmaller than the maximum deviation.

[0042] In an alternative embodiment according to the invention, theextended formatted information may be the measured formattedinformation.

[0043] Preferably, according to the invention, the system includescalculation means that employ a first calculation algorithm with whichthe measured field can be obtained from a universal set containingcharacteristic points and from a virtual reference composed of referencepoints on a reference surface. The image-capture appliance or theimage-restitution appliance includes means for capturing or means forrestituting the universal set, so that an image of characteristic pointscan be produced on the medium. The image of a characteristic point isdefined hereinafter as the characteristic image point.

[0044] The calculating means of the first calculation algorithmadditionally include data-processing means for:

[0045] establishing a bijection between the characteristic image pointsand the reference points, selecting zero or one or more variablecharacteristics,

[0046] referred to hereinafter as selected variable characteristics,among the set of variable characteristics.

[0047] The measured field is composed of:

[0048] the set of pairs composed of one of the reference points and ofthe characteristic image point associated by the bijection, and of

[0049] the value, for the image in question, of each of the selectedvariable characteristics.

[0050] Preferably, according to the invention, the system additionallyincludes analysis means for choosing a mathematical projection,especially a bilinear transformation, between the medium and thereference surface. For the image, the measured field is composed of thevalue of each of the selected variable characteristics and, for eachreference point, it is composed of:

[0051] the pair composed of the reference point and of the mathematicalprojection, onto the reference surface, of the characteristic imagepoint associated by the bijection with the reference point, and/or

[0052] of the pair composed of the characteristic image point associatedby the bijection with the reference point, and of the mathematicalprojection of the reference point onto the medium.

Interpolation to Format an Arbitrary Point

[0053] Preferably, according to the invention, the system additionallyincludes data-processing means for obtaining, from measured formattedinformation, the extended formatted information related to an arbitraryreference point on the reference surface and/or related to an arbitrarycharacteristic image point of the medium, by deducing the formattedinformation related to the arbitrary reference point or to the arbitrarycharacteristic image point.

Variable Focal Length

[0054] Preferably, according to the invention, the system is such thatthe appliance of the appliance chain is provided with at least onevariable characteristic depending on the image, especially the focallength and/or the focusing. Each variable characteristic is capable ofbeing associated with a value to form a combination composed of the setof variable characteristics and values. The system additionallyincludes:

[0055] selection means for selecting predetermined combinations,

[0056] calculating means for calculating measured formatted information,especially by employing the first calculation algorithm for each of thepredetermined combinations selected in this way.

Variable Focal Length—Formatting at an Arbitrary Point

[0057] An argument is defined, depending on the case, as:

[0058] an arbitrary reference point on the reference surface and acombination, or

[0059] an arbitrary characteristic image point of the medium and acombination.

[0060] Preferably, according to the invention, the system additionallyincludes data-processing means for deducing, from measured formattedinformation, the extended formatted information related to an arbitraryargument. It results from the combination of technical features that theformatted information is more compact and resistant to measurementerrors.

Choice of a Threshold for the Deviation, and Formatting According tothis Threshold

[0061] Preferably, according to the invention, the system is such thatthe data-processing means for deducing the extended formattedinformation from measured formatted information include selection meansfor selecting the extended formatted information in such a way that thedeviation is below a first threshold.

Addition of the Deviations to the Formatted Information

[0062] Preferably, according to the invention, the deviations areassociated with the said formatted information. It results from thecombination of technical features that the formatted information can beused by the software for processing images captured by the appliance inorder to obtain images whose residual geometric distortion is known. Itresults from the combination of technical features that the formattedinformation can be used by image-processing software to obtain imagesscheduled to be restituted by an image-restitution appliance with knownresidual geometric distortion.

Choice of Bilinear Transformation

[0063] Preferably, according to the invention, the system additionallyincludes selection means for selecting, on the medium, fourcharacteristic image points such that the quadrilateral defined by thefour characteristic image points is that having a maximum area and acenter of gravity situated in the proximity of the geometric center ofthe image. The mathematical projection is the bilinear transformationthat transforms the four characteristic image points to associatedreference points by bijection at the four characteristic image points.It results from the combination of technical features that it is thenpossible in simple manner to obtain formatted information that can beused by image-processing software to capture or restitute images with asmall change of perspective.

Case of Color Image Distortions

[0064] The image is a color image composed of a plurality of colorplanes. Preferably, according to the invention, the system additionallyincludes data-processing means for producing the measured formattedinformation by employing the first calculation algorithm for at leasttwo of the color planes, by using the same mathematical projection foreach of the color planes. In this way it is possible to use theformatted information and/or measured formatted information to correctthe distortions and/or the chromatic aberrations of the appliance.

[0065] Preferably, according to the invention, the image is a colorimage composed of a plurality of color planes. The system additionallyincludes data-processing means for producing the measured formattedinformation by employing the first calculation algorithm for at leastone of the color planes, by using the same virtual reference for each ofthe color planes. In this way it is possible to use the formattedinformation and/or measured formatted information to correct thechromatic aberrations of the appliance.

DETAILED DESCRIPTION

[0066] Other characteristics and advantages of the invention will becomeapparent upon reading of the description of alternative embodiments ofthe invention, provided by way of indicative and non-limitativeexamples, and of the figures, wherein respectively:

[0067]FIG. 1 illustrates a schematic view of image capture,

[0068]FIG. 2 illustrates a schematic view of image restitution,

[0069]FIG. 3 illustrates a schematic view of the pixels of an image,

[0070]FIGS. 4a and 4 b illustrate two schematic views of a referencescene,

[0071]FIG. 5 illustrates the organizational diagram of the method withwhich the difference between the mathematical image and the correctedimage can be calculated,

[0072]FIG. 6 illustrates the organizational diagram of the method withwhich the best restitution transformation for an image-restitution meanscan be obtained,

[0073]FIG. 7 illustrates a schematic view of the elements composing thesystem according to the invention,

[0074]FIG. 8 illustrates a schematic view of fields of formattedinformation,

[0075]FIG. 9a illustrates a schematic front view of a mathematicalpoint,

[0076]FIG. 9b illustrates a schematic front view of a real point of animage,

[0077]FIG. 9c illustrates a schematic side view of a mathematical point,

[0078]FIG. 9d illustrates a schematic profile view of a real point of animage,

[0079]FIG. 10 illustrates a schematic view of an array of characteristicpoints,

[0080]FIG. 11 illustrates the organizational diagram of the method withwhich the formatted information can be obtained,

[0081]FIG. 12 illustrates the organizational diagram of the method withwhich the best transformation for an image-capture appliance can beobtained,

[0082]FIGS. 13a and 13 b illustrate diagrams with which the productionof a measured field by using bijections can be explained,

[0083]FIGS. 14a and 14 b illustrate diagrams with which the productionof a measured field by using bijections and mathematical projections canbe explained,

[0084]FIGS. 15a and 15 b illustrate a method in which the measured fieldis produced in the form of a polynomial,

[0085]FIGS. 16a and 16 b illustrate an alternative version of a methodof calculating a measured field,

[0086]FIGS. 17 and 18 illustrate methods of interpolation of theformatted information of a point from known formatted information,

[0087]FIGS. 19a to 19 c illustrate alternative versions of the method,with which versions the number of points of calculation of the measuredfield can be minimized,

[0088]FIGS. 20a to 20 d illustrate a method with which the formattedinformation related to a color image can be calculated,

[0089]FIG. 21 illustrates a method related to correction of an imagethat has been deformed by a projection,

[0090]FIG. 22 illustrates an alternative version of the method, withwhich version the number of points of calculation can be minimized inthe case of correction of a geometric distortion,

[0091]FIGS. 23a to 23 c illustrate a method with which unprocessed zonesof a corrected image can be eliminated,

[0092]FIG. 24 illustrates formatted information related to the geometricdistortions of an appliance APP1 of an appliance chain P3,

[0093]FIG. 25 illustrates a practical embodiment of a system accordingto the invention.

[0094]FIG. 1 illustrates a scene 3 containing an object 107, a sensor101 and sensor surface 110, an optical center 111, an observation point105 on a sensor surface 110, an observation direction 106 passingthrough observation point 105, optical center 111, scene 3, and asurface 10 geometrically associated with sensor surface 110.

[0095]FIG. 2 illustrates an image 103, an image-restitution means 19 anda restituted image 191 obtained on the restitution medium 190.

[0096]FIG. 3 illustrates a scene 3, an image-capture appliance 1 and animage 103 composed of pixels 104.

[0097]FIGS. 4a and 4 b illustrate two alternative versions of areference scene 9.

[0098]FIG. 5 illustrates an organizational diagram employing a scene 3,a mathematical projection 8 giving a mathematical image 70 of scene 3, areal projection 72 giving an image 103 of scene 3 for thecharacteristics 74 used, a parameterizable transformation model 12giving a corrected image 71 of image 103, the corrected image 71exhibiting a difference 73 compared with mathematical image 70.

[0099]FIG. 6 illustrates an organizational diagram employing an image103, a real restitution projection 90 giving a restituted image 191 ofimage 103 for the restitution characteristics 95 used, a parameterizablerestitution transformation model 97 giving a corrected restitution image94 of image 103, a mathematical restitution projection 96 giving amathematical restitution image 92 of corrected restitution image 94 andexhibiting a restitution difference 93 compared with restituted image191.

[0100]FIG. 7 illustrates a system comprising an image-capture appliance1 composed of an optical system 100, of a sensor 101 and of anelectronic unit 102. FIG. 7 also illustrates a memory zone 16 containingan image 103, a database 22 containing formatted information 15, andmeans 18 for transmission of completed image 120 composed of image 103and formatted information 15 to calculating means 17 containingimage-processing software 4.

[0101]FIG. 8 illustrates formatted information 15 composed of fields 90.

[0102]FIGS. 9a to 9 d illustrate a mathematical image 70, an image 103,the mathematical position 40 of a point, and the mathematical shape 41of a point, compared with the real position 50 and the real shape 51 ofthe corresponding point of the image.

[0103]FIG. 10 illustrates an array 80 of characteristic points.

[0104]FIG. 11 illustrates an organizational diagram employing an image103, the characteristics 74 used, and a database 22 of characteristics.The formatted information 15 is obtained from the characteristics 74used and stored in database 22. The completed image 120 is obtained fromimage 103 and formatted information 15.

[0105]FIG. 12 illustrates an organizational diagram employing areference scene 9, a mathematical projection 8 giving a synthetic imageclass 7 of reference scene 9, and a real projection 72 giving areference image 11 of reference scene 9 for the characteristics 74 used.This organizational diagram also employs a parameterizabletransformation model 12 giving a transformed image 13 of reference image11. Transformed image 13 exhibits a deviation 14 compared with syntheticimage class 7.

Appliance

[0106] Referring in particular to FIGS. 2, 3 13 a, 13 b and 24, adescription will be given of the concept of appliance APP1. Within themeaning of the invention, an appliance APP1 may be in particular:

[0107] an image-capture appliance 1, as shown on FIG. 3 or animage-capture appliance as shown on FIG. 13a, such as a disposable photoappliance, a digital photo appliance, a reflex appliance, a scanner, afax machine, an endoscope, a camcorder, a surveillance camera, a webcam,a camera integrated into or connected to a telephone, to a personaldigital assistant or to a computer, a thermal camera or an echographicappliance,

[0108] an image-restitution appliance APP2 as illustrated on FIG. 13b orimage-restitution means 19, as illustrated on FIG. 2, such as a screen,a projector, a television set, virtual-reality goggles or a printer,

[0109] a human being having vision defects, for example astigmatism,

[0110] an appliance which it is hoped can be emulated, to produce imageshaving, for example, an appearance similar to those produced by anappliance of the Leica brand,

[0111] an image-processing device, such as zoom software, which has theedge effect of adding blurring,

[0112] a virtual appliance equivalent to a plurality of appliances APP1,

[0113] A more complex appliance APP1, such as a scanner/fax/printer, aphoto-printing Minilab, or a videoconferencing appliance can be regardedas an appliance APP1 or as a plurality of appliances APP1.

Appliance Chain

[0114] Referring in particular to FIG. 24, a description will now begiven of the concept of appliance chain P3. An appliance chain P3 isdefined as a set of appliances APP1. The concept of appliance chain P3may also include a concept of order.

[0115] The following examples constitute appliance chains P3:

[0116] a single appliance APP1,

[0117] an image-capture appliance and an image-restitution appliance,

[0118] a photo appliance, a scanner or a printer, for example in aphoto-printing Minilab,

[0119] a digital photo appliance or a printer, for example in aphoto-printing Minilab,

[0120] a scanner, a screen or a printer, for example in a computer,

[0121] a screen or projector, and the eye of a human being,

[0122] one appliance and another appliance which it is hoped can beemulated,

[0123] a photo appliance and a scanner,

[0124] an image-capture appliance and image-processing software,

[0125] image-processing software and an image-restitution appliance,

[0126] a combination of the preceding examples,

[0127] another set of appliances APP1.

Defect

[0128] Referring in particular to FIG. 24, a description will now begiven of the concept of defect P5. A defect P5 of appliance APP1 isdefined as a defect related to the characteristics of the optical systemand/or of the sensor and/or of the electronic unit and/or of thesoftware integrated in an appliance APP1; examples of defects P5 includegeometric distortion, blurring, vignetting, chromatic aberrationsrendering of colors, flash uniformity, sensor noise, grain, astigmatismand spherical aberration.

Image

[0129] Referring in particular to FIG. 13a, a description will now begiven of the concept of image I. Image I is defined as an image capturedor modified or restituted by an appliance APP1. Image I may originatefrom an appliance APP1 of appliance chain P3. Image I may be addressedto an appliance APP1 of appliance chain P3. In the case of animatedimages, such as video images, composed of a time sequence of fixedimages, image I is defined as one fixed image of the sequence of images.

Formatted Information

[0130] Referring in particular to FIG. 24, a description will now begiven of the concept of formatted information IF. Formatted informationIF is defined as data related to the defects P5 of one or moreappliances APP1 of appliance chain P3 and making it possible tocalculate a transformed image by taking into account the defects P5 ofthe appliance APP1. To produce the formatted information IF, there maybe used various methods based on measurements, and/or captures orrestitution of references, and/or simulations.

[0131] To produce the formatted information IF, it is possible, forexample, to use the method described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for reducing updatefrequency of image processing means”. That application describes amethod for reducing the update frequency of image-processing means, inparticular software and/or a component. The image-processing means makeit possible to modify the quality of the digital images derived from oraddressed to an appliance chain. The appliance chain is composed inparticular of at least one image-capture appliance and/or at least oneimage-restitution appliance. The image-processing means employ formattedinformation related to the defects of at least one appliance of theappliance chain. The formatted information IF depends on at least onevariable. The formatted information makes it possible to establish acorrespondence between one part of the variables and of the identifiers.By means of the identifiers it is possible to determine the value of thevariable corresponding to the identifier by taking the identifier andthe image into account. It results from the combination of technicalfeatures that it is possible to determine the value of a variable,especially in the case in which the physical significance and/or thecontent of the variable are known only after distribution of theimage-processing means. It also results from the combination oftechnical features that the time between two updates of the correctionsoftware can be spaced apart. It also results from the combination oftechnical features that the various economic players that produceappliances and/or image-processing means can update their productsindependently of other economic players, even if the latter radicallychange the characteristics of their product or are unable to force theirclient to update their products. It also results from the combination oftechnical features that a new functionality can be deployedprogressively by starting with a limited number of economic players andpioneer users.

[0132] To produce the formatted information IF, there may be used, forexample, the method described in the International Patent Applicationfiled on the same day as the present application and entitled “Methodand system for providing formatted information to image-processingmeans, according to a standard format.” That application describes amethod for providing formatted information IF to image-processing means,in particular software and/or components, according to a standardformat. The formatted information IF is related to the defects of anappliance chain P3. The appliance chain P3 comprises in particular atleast one image-capture appliance 1 and/or an image-restitutionappliance 19. The image-processing means use the formatted informationIF to modify the quality of at least one image derived from or addressedto the appliance chain P3. The formatted information IF comprises datacharacterizing the defects P5 of the image-capture appliance 1, inparticular the distortion characteristics, and/or data characterizingthe defects of the image-restitution appliance 19, in particular thedistortion characteristics.

[0133] The method includes the stage of filling in at least one field ofthe standard format with the formatted information IF. The field isdesignated by a field name, the field containing at least one fieldvalue.

[0134] To use the formatted information IF, it is possible, for example,to use the method described in the International Patent Applicationfiled on the same day as the present application in the name of VisionIQ and entitled “Method and system for modifying the quality of at leastone image derived from or addressed to an appliance chain”. Thatapplication describes a method for modifying the quality of at least oneimage derived from or addressed to a specified appliance chain. Thespecified appliance chain is composed of at least one image-captureappliance 1 and/or at least one image-restitution appliance. Theimage-capture appliances 1 and/or the image-restitution 19 appliances 19being progressively introduced on the market by separate economicplayers belong to an indeterminate set of appliances. The appliancesAPP1 of the set of appliances exhibit defects P5 that can becharacterized by the formatted information. For the image in question,the method includes the following stages:

[0135] the stage of compiling directories of the sources of formattedinformation related to the appliances of the set of appliances,

[0136] the stage of automatically searching for specific formattedinformation related to the specified appliance chain among the formattedinformation compiled in this way,

[0137] the stage of automatically modifying the image I by means ofimage-processing software and/or image-processing components, whiletaking into account the specific formatted information obtained in thisway.

[0138] To use the formatted information IF, it is possible, for example,to use the method described in the International Patent Applicationfiled on the same day as the present application in the name of VisionIQ and entitled “Method and system for calculating a transformed imagefrom a digital image and formatted information related to a geometrictransformation”. That application describes a method for calculating atransformed image from a digital image and formatted information IFrelated to a geometric transformation, especially formatted informationIF related to the distortions and/or chromatic aberrations of anappliance chain. The method includes the stage of calculating thetransformed image from an approximation of the geometric transformation.It results therefrom that the calculation is economical in terms ofmemory resources, in memory bandpass, in calculating power and thereforein electricity consumption. It also results therefrom that thetransformed image does not exhibit any visible or annoying defect asregards its subsequent use.

[0139] To use the formatted information IF, it is possible, for example,to use the method described in the International Patent Applicationfiled on the same day as the International Patent Application for thisinvention under number ______ in the name of Vision IQ entitled: Methodand system for correcting the chromatic aberrations of a color imageconstructed by means of an optical system. In this patent, there isdescribed a method for correcting the chromatic aberrations of a colorimage composed of a plurality of digitized color planes. The color imagewas constructed by means of an optical system. The method includes thefollowing stages:

[0140] the stage of modeling and of correcting, at least in part, thegeometric anomalies of the digitized color planes, in such a way as toobtain corrected digitized color planes,

[0141] the stage of combining the corrected digitized color planes, insuch as way as to obtain a color image corrected, in whole or in part,for chromatic aberrations.

Variable Characteristic

[0142] A description will now be given of the concept of variablecharacteristic. According to the invention, a variable characteristic isdefined as a measurable factor, which is variable from one image I toanother that has been captured, modified or restituted by the sameappliance APP1, and which has an influence on defect P5 of the imagethat has been captured, modified or restituted by appliance APP1,especially:

[0143] a global variable, which is fixed for a given image I, an examplebeing a characteristic of appliance APP1 at the moment of capture orrestitution of the image, related to an adjustment of the user orrelated to an automatic function of appliance APP1,

[0144] a local variable, which is variable within a given image I, anexample being coordinates x, y or rho, theta in the image, making itpossible to apply, if need be, local processing that differs dependingon the zone of the image I.

[0145] A measurable factor which is variable from one appliance APP1 toanother but which is fixed from one image I to another that has beencaptured, modified or restituted by the same appliance APP1 is notgenerally considered to be a variable characteristic. An example is thefocal length for an appliance APP1 with fixed focal length.

[0146] The formatted information IF may depend on at least one variablecharacteristic.

[0147] By variable characteristic there can be understood in particular:

[0148] the focal length of the optical system,

[0149] the redimensioning applied to the image (digital zoom factor:enlargement of part of the image; and/or under-sampling: reduction ofthe number of pixels of the image),

[0150] the nonlinear brightness correction, such as the gammacorrection,

[0151] the enhancement of contour, such as the level of deblurringapplied by appliance APP1,

[0152] the noise of the sensor and of the electronic unit,

[0153] the aperture of the optical system,

[0154] the focusing distance,

[0155] the number of the frame on a film,

[0156] the underexposure or overexposure,

[0157] the sensitivity of the film or sensor,

[0158] the type of paper used in a printer,

[0159] the position of the center of the sensor in the image,

[0160] the rotation of the image relative to the sensor,

[0161] the position of a projector relative to the screen,

[0162] the white balance used,

[0163] the activation of a flash and/or its power,

[0164] the exposure time,

[0165] the sensor gain,

[0166] the compression,

[0167] the contrast,

[0168] another adjustment applied by the user of appliance APP1, such asa mode of operation,

[0169] another automatic adjustment of appliance APP1,

[0170] another measurement performed by appliance APP1.

Value of a Variable Characteristic

[0171] A description will now be given of the concept of value of avariable characteristic. The value of a variable characteristic isdefined as the value of the variable characteristic at the moment ofcapture, modification or restitution of a specified image.

Parameterizable Model

[0172] Within the meaning of the invention, a parameterizable model orparameterizable transformation model or parameterizable transformationis defined as a mathematical model that can depend on variablecharacteristics related to one or more defects P5 of one or moreappliances APP1. The formatted information IF related to a defect P5 ofan appliance may be presented in the form of parameters of aparameterizable model that depends on variable characteristics.

Formatted Information Related to Geometric Distortions

[0173]FIG. 13a illustrates an organizational diagram employing:

[0174] a universal set M, which may be the foregoing reference scene 9,

[0175] a medium SC containing an image. In the case of an image-capturesystem, surface SC may be that of a sensor (such as a CCD) or, in thecase of an image-restitution system, this surface may be that of aprojection screen or that of a paper sheet of a printer.

[0176] a virtual reference surface SR (equivalent to the foregoingsurface 10), containing a virtual reference R or a virtual referenceimage, which may be a synthetic image of synthetic-image class 7 in theforegoing.

[0177] By means of an appliance APP1 or of an appliance chain P3, imageI (defined as reference image 11 in the foregoing) is obtained, fromuniversal set M, on a medium SC, which may be a sensor surface. Anappliance chain is a set of appliances with which an image can beobtained. For example, an appliance chain App1/App2/App3 will be able toinclude an image-capture appliance, a scanner, a printing appliance,etc.

[0178] Image I therefore contains defects P5 and, in particular,geometric distortions related to these appliances APP1.

[0179] Virtual reference R is deduced directly from M, and must beregarded as perfect or quasi-perfect. It may be identical orquasi-identical to M, or instead may exhibit differences, as will beseen farther on.

[0180] As an example, we can explain the relationship between universalset M and reference surface R as follows: To points PP1 to PPm ofuniversal set M there correspond reference points PR1 to PRm in virtualreference R of reference surface SR as well as characteristic imagepoints PT1 to PTm of image I of medium SC.

[0181] According to a practical example of the invention, there istherefore provided a stage of construction of image I by means ofappliance APP1 or of appliance chain P3.

[0182] In the course of a subsequent stage, there is chosen a certainnumber of points PTi, PRi. These points are chosen in limited numbersand are situated in characteristic zones of universal set M, of image Iand of virtual reference R. A bijection is then established between thepoints PTi of the image and the points PRi of the virtual reference.Thus, to each chosen point PTi there is made to correspond acorresponding point PRi, and vice versa.

[0183] In the course of another stage, it is possible but not necessaryto choose variable characteristics of the appliance APP1 (or of theappliance chain) among those used to obtain image I with appliance APP1.The variable characteristics of an appliance or of an appliance chaincan include the focal length of the optical system of an appliance, thefocus, the aperture, the number of the photo in a set of photos, thedigital zoom, and the characteristics of partial capture of an image(“crop” in English terminology), etc.

[0184] The following information set comprises a measured field DH withwhich, for correction of an image, it will be possible subsequently touse:

[0185] the bijection, or in other words the set of pairs of points Ptiand PRi that have been chosen and that correspond via the foregoingbijection,

[0186] the set of chosen variable characteristics.

[0187] By means of this information, which comprises a measurementfield, it will be possible to obtain measured formatted information.

[0188] In an alternative embodiment, it is possible to use software thatsimulates the appliance, especially optical simulation software, or touse an optical measuring bench to calculate the points PTi from thepoints PPi of universal set M or from the points PPi of a model ofuniversal set M.

[0189]FIG. 14a illustrates another form of obtaining a measured field.

[0190] This FIG. 14a shows universal set M, reference surface SR andmedium SC.

[0191] As in the foregoing, image I is constructed on medium SC by meansof an appliance APP3. Then the bijection described in the foregoing isapplied.

[0192] A mathematical projection, preferably a bilinear transformation,is then established between a point of medium SC and a point ofreference surface SR.

[0193] In FIG. 14b it is seen that, for each point PRj of the referencesurface, there can be obtained a point H(PRj) of the image bymathematical projection. Preferably, for two points PRj and PTj of apair associated by bijection, there is a point H(PRj), the mathematicalprojection of PRj onto medium SC.

[0194] Under these conditions, a more complete measured field isobtained by adding the established mathematical-projection formulas tothe field information.

[0195] A measured field DH therefore contains:

[0196] any variable characteristics that have been chosen;

[0197] for different reference points PR, the mathematical projectionH(PRj) of the reference point PRj onto medium SC thus provides a newpoint H(PRj) associated with the corresponding point PTj by bijection.Thus, in the measured field, there exists a series of pairs of pointsassociated by bijection, wherein one point in each pair is themathematical projection of the other point of the pair.

[0198] Thus the measured field DH may also be composed of:

[0199] chosen variable characteristics;

[0200] pairs that are each composed of a point PT of the referencesurface and a point H(PR) representing the mathematical projection ofthe point PR associated by bijection with the point PT of medium SC.

[0201] The measured field DH obtained in this way for an image mayinclude, as a factor, the variable characteristics for the set of pairsof points obtained, in such a way as to achieve a gain of memory space.

[0202] According to another alternative embodiment of the invention, themeasured field DH may be composed of:

[0203] chosen variable characteristics;

[0204] pairs of points PT and of mathematical projections of points PR(associated by bijection with the points PT) on medium SC;

[0205] and pairs of points PR and of mathematical projections of pointsPT (associated by bijection with the points PR) on reference surface SR.

[0206] As in the foregoing, it is possible with the measured field DH toobtain measured formatted information.

[0207] By means of the foregoing examples of methods and systemsillustrated by FIGS. 13a to 14 b, it is possible to obtain a field ofmeasurements defined as measured field DH, which is composed of as manyinformation sets as there exist chosen points of image I and of virtualreference R.

[0208] With this measured field for image I, there is composed a set ofmeasured formatted information IFM. An item of measured formattedinformation of a point PTj will therefore include, for example:

[0209] the fixed characteristics of the appliance or appliances beingused;

[0210] the chosen variable characteristics;

[0211] the X, Y position of point PTj in the image;

[0212] the mathematical projection of the corresponding point PRj bybijection.

[0213] It will be noted that identity is a particular mathematicalprojection that can be used, for example in scanners.

[0214] The use of the system will lead to the need to process a largenumber of points and thus a large volume of information. To makeoperation of the system more flexible, to accelerate processing and/orto be resistant to measurement errors, the method and system illustratedby FIGS. 15a and 15 b provide for deducing, from items of measuredformatted information IFE1 to IFMn, items of extended formattedinformation IFE1 to IFEm belonging to a surface that can be representedby a function chosen within a space of limited dimension, such as apolynomial of limited order chosen among the class of polynomials offinite degree, or a spline function of appropriate degree.

[0215]FIGS. 15a and 15 b illustrate simplified examples corresponding tocases in which the measured formatted information is a function of onlya single variable. The invention is applicable in the same way when theformatted information is a function of a plurality of variables, as isgenerally the case.

[0216]FIGS. 15a and 15 b illustrate simplified examples corresponding tocases in which the measured formatted information is scalar and is afunction of only two variables (X, Y). The invention is applicable inthe same way when the formatted information is vectorial and is afunction of more than two variables, as is generally the case.

[0217] In FIG. 15b, the different coordinates of the points of an imageare illustrated in plane IM. The measured formatted information IFE1 islocated at the point with coordinates X1, Y1. At each point of plane IM,there is therefore an item of formatted information having a particularvalue. The invention comprises calculating a parameterizable model suchas a polynomial surface SP. A particular way of calculating SP may be tocalculate this surface by passing through or by passing in proximity toall of the extremities of the measured formatted information. Anotherprocedure could be to preserve the geometric characteristics (notnecessarily Euclidian) of a subset of points of M, such as the alignmentof points on a straight line, or any curve of specifiedparameterization. Under these conditions, the system will be able to usea parameterizable model during processing of an image, instead ofresorting to a large volume of measured formatted information.

[0218] The difficulty lies in finding a surface SP that passes throughall of the points or in proximity to all of these points. The assumptionis provided that a deviation EC could exist between an item of measuredformatted information IFM and an item of extended formatted informationIFE. In addition, it is decided that such an EC must not exceed acertain threshold dS. Under these conditions it will be appropriate tomake a polynomial surface pass through all of the points of the measuredformatted information IFM±dS.

[0219] The choice of this threshold will be made appropriately with thefilming errors, the measurement errors, the level of precision requiredfor the correction, etc.

[0220] The method and the system employed will be able to provide forthe use of a specified number of parameterizable models that can bewritten, for example, in the form of polynomials. It is provided thatthese models are classified in order of increasing complexity.

[0221] Thereafter, given possession of a set of measured information,each model is tested by starting preferably from the simplest model (thepolynomial of lowest order), until there is obtained a model thatdefines, at the intersection of the polynomial surface and the directionof each item of measured formatted information, an item of extendedformatted information whose deviation EC compared with the item ofmeasured formatted information is below the threshold dS.

[0222] The method and system illustrated schematically by FIGS. 15a and15 b are designed to obtain extended measured formatted information.However, the invention could be limited to using only the measuredformatted information as formatted information. It is also possible toprovide for the use, as formatted information, of the measured formattedinformation and the extended measured formatted information.

[0223] Regardless of the case, it is also possible to provide forassociating, with the formatted information, the deviations EC foundbetween measured formatted information and extended formattedinformation. In this way the formatted information can be used byimage-processing software to obtain images whose residual geometricdistortion is known, whether it be for images captured by animage-capture appliance or for images restituted by an image-restitutionappliance.

[0224] Referring to FIGS. 16a and 16 b, a description will now be givenof an alternative version of calculation of the measured field D(H) ofan image I.

[0225] According to the organizational diagram of algorithm AC2 of FIG.16a, in which a universal set M such as that of FIG. 14a is available,the capture of this universal set M by means of appliance APP3 isundertaken in the course of a first stage ET2.1. Image I is obtained onmedium SC. Also available is a virtual reference R on a referencesurface SR. This virtual reference R in principle represents universalset M, exactly or quasi-exactly.

[0226] In the course of stage ET2.2, a bijection is established betweenthe characteristic image points PT of image I of medium SC and thereference points PR of virtual reference R of reference surface SR (seealso FIG. 14a).

[0227] In the course of stage ET2.3, there is chosen a mathematicalprojection such as a bilinear transformation between different points ofmedium SC (or of image I) and different points of reference surface SR(or of virtual reference R).

[0228] In the course of stage ET2.4, a vector characterizing thegeometric distortion defect is calculated for each characteristic imagepoint PT or for each reference point PR. FIG. 16b illustrates this stageof the method by an example of practical implementation. This figureshows different values of reference points PR distributed over referencesurface SR. With each point PR there is associated the mathematicalprojection H(PT) of the point PT associated by bijection with PR. Thevector VM having origin PR and extremity H(PT) is calculated for eachpoint.

[0229] In the course of stage ET2.5, the measured field is calculated.

[0230] This field DH, which can also be defined as a field of measuredvectors, is composed of:

[0231] pairs of chosen points PT and PR associated by bijection;

[0232] the vector calculated for each point.

[0233] The field DH may also be composed more simply of:

[0234] reference point PR of SR and/or characteristic image point PT ofSC and/or of the mathematical projection of reference point PR onto SC(or conversely of the projection of characteristic image point PT ontoSR),

[0235] and of the vector calculated in the foregoing and associated withthat point.

[0236] The measured field DH may also include variable characteristicsof appliance APP1 (APP2).

[0237] The field DH may also be composed of an approximation of measuredinformation. In fact, to obtain a gain of calculation space and/or time,the measured formatted information can be quantified by means of alimited number of bits (3 bits, for example).

[0238] It will be noted that, during stage ET2.4, the calculated vectorVM can be that having as origin the mathematical projection H(PT) of thepoint PT onto surface SR and as extremity the point PR.

[0239] Alternatively, the vector VM may be that having as origin thecharacteristic point PT and as extremity the mathematical projection ofthe point PR associated by bijection. Conversely, the vector VM may bethat having as origin the mathematical projection of a point PRassociated by bijection with a point PT and, as extremity, this point PTor another combination that employs the said points.

[0240] In the foregoing, it has been seen that formatted informationcould contain variable characteristics. In fact, a combination ofvariable characteristics, such as a combination of focal length,focusing, diaphragm aperture, capture speed, aperture, etc. may beinvolved. It is difficult to imagine how the formatted informationrelated to different combinations can be calculated, all the more sobecause certain characteristics of the combination, especially such asthe focal length and the distance, can vary continuously.

[0241] As illustrated in FIG. 17, the invention provides for calculatingthe formatted information by interpolation from measured formattedinformation for combinations of known variable characteristics.

[0242] For example, in the simplified illustration of FIG. 17, eachplane contains the measured formatted information of an image for aspecified value of combinations. For example, the plane f=2 correspondsto the combination of “focal length=2, distance=7, capture speed=1/100”.The plane f=10 corresponds to the combination of “focal length=10,distance=7, capture speed=1/100”. The plane f=50 corresponds to thecombination of “focal length=50, distance=7, capture speed=1/100”.

[0243] For an arbitrary point PQT of the medium or an arbitrary pointPQR of the reference surface, whose variable characteristics containamong others the combination of “focal length=25, distance=7 and capturespeed=1/100”, a value of extended formatted information is interpolatedbetween the two planes f=10 and f=50 of FIG. 17 and, in particular,assuming that the planes of FIG. 17 represent measured formattedinformation of points PT of the image, between the two points PT(10) andPT(50) of planes f=10 and f=50.

[0244] A practical example of the invention will therefore employ thecalculation of a measured field, such as that described in connectionwith FIGS. 13 or 14, then the calculation of formatted information, asdescribed in connection with FIGS. 15a to 16 b. These differentcalculations and the corresponding stages will be undertaken fordifferent combinations of variable characteristics and/or for differentcombinations with an associated value. Then, for an arbitrary point (PQTor PQR) or a set of arbitrary points of an image I captured by means ofan arbitrary but known combination, extended formatted information willbe interpolated between two planes of measured formatted information.

[0245] In FIG. 17 there is considered a case in which the point forwhich formatted information is to be calculated had the same coordinatesX and Y as the points for which the measured formatted information isknown.

[0246]FIG. 18 illustrates the case of a search for the measuredformatted information of an arbitrary point PQRi or PQTi, which issituated between the planes f=10 and f=50 and whose coordinates do notcorrespond to coordinates of points of the planes f=10 and f=50.

[0247] To each point there is assigned an argument Ai containing atleast the coordinates Xi and Yi of the point as well as thecharacteristics of a combination of variable characteristics.

[0248] The plane f=2 corresponds to a combination C1.0 of variablecharacteristics. The plane f=10 corresponds to a combination C2.0 andthe plane f=50 corresponds to a combination Cm.0.

[0249] Each point of the plane f=2 has as argument:

[0250] “coordinates X, Y; combination C1.0”.

[0251] The point PQRi or PQTi for which formatted information is beingsought has as argument:

[0252] “coordinates Xi, Yi; combination Ci”.

[0253] Under these conditions, the method and system will perform, forexample, an interpolation between the items of measured formattedinformation of planes f=10 and f=50.

[0254] For an arbitrary point PQT/PQR, it is sufficient, for example, toreinject the argument (X, Y, focal length, distance, aperture, iso,speed, flash, etc.) related to this point into the parameterizable modelin order to find the formatted information related to this point.

[0255] An effective way of calculating the bilinear transformationbetween reference surface SR and medium surface SC may be achieved bychoosing, on medium SC and on reference surface SR, four points PTm1 toPTm4 and PRm1 to PRm4 that correspond by bijection and that, forexample, are at the peripheral limits of medium SC and of referencesurface SR. The positions of these points are chosen, for example, insuch a way as to maximize the areas included between these points.

[0256] In addition, as illustrated in FIG. 19c, the positions of thesepoints are such that the intersection of the diagonals of thequadrilaterals defined by these points is located at the center or closeto the center of the quadrilaterals.

[0257] There is then calculated a mathematical projection (such as abilinear transformation) with which the four characteristic points PTm.1to PTm.4 can be transformed to the four reference points PRm.1 to PRm.4.

[0258] This mathematical projection will be associated with theformatted information of the image.

[0259] It will be possible to use this formatted information inimage-processing software to correct the geometric distortions ofperspective or to restitute images with little change of perspective.

[0260] Another way of choosing the four points PTm.1 to 4 and PRm1. to 4comprises taking, within image I, four points PTm.1 to 4 in such a waythat they form a quadrilateral that is as close as possible, except fora scaling factor, to the quadrilateral formed by the points H(PRm.1 to4), which are mathematical projections of the points PRm.1 to 4corresponding via bijections to the points PTm.1 to 4.

[0261] Referring to FIGS. 20a to 20 d, a description will be given ofmethods for calculating formatted information related to color images. Acolor image can be regarded as being composed of a plurality ofmonochromatic images. Traditionally, it can be considered that a colorimage is a trichromatic image composed of three monochromatic images(red, green, blue). It is known in optics that the distortions inducedby the optical systems and the light-transmission media induce differenteffects on the different wavelengths. In a trichromatic image, the samephysical defect of an appliance will therefore induce differentdistortions on the image being transported by light of red wavelength,on that being transported by light of green wavelength and on that beingtransported by light of blue wavelength.

[0262] As illustrated in FIG. 20a, starting from a trichromaticuniversal set M, to which there corresponds a quasi-identical virtualreference R, there will correspond, in image I, three superposed imagesR, G and B, which have been illustrated separately on the planes SCR,SCG and SCB. The three images IR, IG and IB exhibit differentdistortions, leading to a trichromatic image that exhibits bothgeometric distortions and chromatic aberrations.

[0263]FIG. 20b illustrates the principle of the method and system withwhich there can be obtained formatted information that will permitimage-processing software to correct distortions and/or chromaticaberrations.

[0264] According to this method and system, one item of formattedinformation per color will be calculated for each trichromatic point ofthe image. It will therefore be considered that it is appropriate tocorrect as many monochromatic images as there are colors. In thetrichromatic example, the calculations will be performed as if therewere three images to be corrected.

[0265] For calculation of the formatted information of the three imagesIR, IG and IB, there are used the same methods and systems as thosedescribed in relation to FIGS. 13a to 19 c.

[0266]FIG. 20b illustrates surface SR with a virtual reference Rcontaining trichromatic points PR(RGB) and also illustrates thedecomposition of image I into three monochromatic images IR, IG, IB,each containing the points PTR, PTG, PTB of a single color.

[0267] One way of calculating the formatted information related to atrichromatic point is to use the same virtual reference R for the threecolor planes. Thus three mathematical projections are used: amathematical projection HR for red point PTR, a mathematical projectionHG for green point PTG and a mathematical projection HB for blue pointPTB, as illustrated in FIG. 20b.

[0268] A second approach for calculating the formatted informationrelated to a trichromatic point is to use the choice of a singlemonochromatic image IR or IG or IB, from which a single mathematicalprojection HR or HG or HB is calculated. For example, the formattedinformation is extracted solely from image IR, and this formattedinformation will be retained for the green and blue images. Thisapproach is economic in terms of calculation time and memory space.

[0269] By means of the formatted information obtained in this way, itwill then be possible to correct the geometric distortions.

[0270] As shown in FIG. 20c, another approach is to use the same virtualreference R and to calculate formatted information for each color planeby using the same mathematical projection, defined optionally, onto oneof the monochromatic planes. For example, only the mathematicalprojection HR related to the red point is calculated. This mathematicalprojection is then applied to the three red, green and blue points tocalculate the formatted information of these three points. In this case,it will be possible for image-processing software to correct both thegeometric distortions and the chromatic aberrations of the image.

[0271] Another approach, illustrated by FIG. 20d, consists in:

[0272] For the image of a specified color, such as the red image IR,calculating the formatted information by using a virtual reference Rassumed to be perfect and a mathematical projection H(R) of the pointsof the virtual reference onto the surface of the red image IR, thusmaking it possible to correct the distortions of the red image.

[0273] For the images of the other colors, such as the green and blueimages IG and IB, using the foregoing color image—the red image IRaccording to the adopted example—as virtual reference R′ and undertakingthe same mathematical projection H(IRd) of the points of this red imageonto the surfaces of the green image IG and then blue image IB.Preferably, this mathematical projection will be an identity (oridentity projection) of the points of the red image onto the green andblue images. In this way it will be possible to suppress the differences(chromatic aberrations) between the red, green and blue images. Theformatted information of the points of the green and blue images willtherefore be able to contain the mathematical projection of the pointsof virtual reference R onto the red image as well as the mathematicalprojections (identity) of the red image onto the green and blue imagesrespectively. This approach may make it possible, as the case may be, tocorrect the distortions alone by using only the formatted informationextracted from the red image, the chromatic aberration alone by usingonly the formatted information related to the green and blue images, orthe two phenomena simultaneously by using all of the formattedinformation.

[0274] It will also be noted in the foregoing description that it willbe possible to make the choice of thresholds for each parameterizablemodel related to the chromatic aberrations in a manner different fromthat related to the geometric distortions, in such a way as to achievegreater or lesser precision in compensating for that defect.

[0275] It will also be noted that the choice of mathematical projectionsmay be made for only part of the image. For example, if image I andvirtual reference R have shapes such as illustrated in FIG. 22, and if aperspective effect is to be restituted in the image, the mathematicalprojection of points PR onto medium SC will be possible by using onlyfour points PT1 to PT4 and PR1 to PR4, which are sufficient to define abilinear transformation. The other points of the image will then followthis mathematical projection for the purpose of obtaining an imageexhibiting a perspective effect, such as the image IC1 illustrated inFIG. 22. This choice of mathematical projection may be generalized so asto obtain a particular effect on the image to be corrected byimage-processing software by means of formatted information calculatedin this way.

[0276] It will be noted that, although chromatic information has beenused for correction of distortions, it would also be possible to usebrightness information.

[0277] In the foregoing, it was considered that virtual reference R wasquasi-identical to universal set M. If it is considered that virtualreference R is exactly identical to universal set M, it will be possibleto calculate formatted information that will make it possible to correctimage I so that it is the exact replica of universal set M.

[0278] As illustrated in FIG. 21, it may be provided that virtualreference R is deformed compared with universal set M. For example, thevirtual reference has a trapezoidal shape, whereas universal set M has arectangular shape. The formatted information that will be obtained willmake it possible to correct image I to induce a trapezoidal deformationon the corrected image. An example of application of such an arrangementexists in overhead projectors, where it will be possible to correct thewell-known deformation induced by these appliances during projectionbecause of the fact that the axis of the projection beam is notperpendicular to the plane of the screen.

[0279] It is also possible to deform the virtual reference bydistortions, to induce characteristics and even defects obtained withappliances other than those obtained by the appliances with whichconstruction of image I was possible. As an example, it will be possibleto induce, in the virtual reference, characteristics of improvedappliances or alternatively of old appliances, to impart a particularappearance to the corrected image. The formatted information, themeasured formatted information or the extended measured formattedinformation obtained with such a virtual reference integrate thedistortions that were induced in the virtual reference, in such a waythat the formatted information and/or the measured formatted informationcan be used by software for processing images captured by a firstimage-capture appliance to obtain images whose quality, in terms ofdistortions, is comparable to that of a second image-capture appliance.This technique is also applicable to image restitution, by consideringthat image-processing software can then restitute, by means of a firstrestitution appliance, an image whose quality, in terms of distortions,is comparable to that provided by a second restitution appliance.

[0280] In addition, it may be provided that the formatted informationobtained, when used by image-processing software, will lead to thepresence of unprocessed zones on the periphery of the corrected image.As an example, an uncorrected image I illustrated in FIG. 23c will beable to yield a corrected image Ic such as illustrated in FIG. 23b, andwhich possesses unprocessed zones ZN, which are represented in black inFIG. 23b.

[0281] It will therefore be possible to modify the formatted informationbeforehand, to obtain an enlargement effect Ic′ such as illustrated inFIG. 23c, in such a way as to eliminate the unprocessed zones.

[0282] For practical purposes during calibration and during calculationof formatted information, it will advantageously be provided that thecalculations will be performed and the methods described will be appliedto a plurality of images, and then an average of the results obtainedwill be taken, thus eliminating beforehand, if need be, the results thatseem aberrant.

[0283] In addition, in the case of combinations involving variablecharacteristics that can have a large number of values, it is possibleto provide for limiting the number of combinations. For this purpose, itis provided that an analysis into principal components will beundertaken for these variable characteristics. It involves searching fora particular direction or directions of the components corresponding tothese variable characteristics for which the distortions aresubstantial. For other directions, regardless of what the other variablecharacteristics are, it will probably be observed that little or novariation of distortion exists. Thus these other directions will not betaken into account.

[0284] In the preferential direction or directions, the number ofreference images will be chosen according to different criteria, such asthe fact of being able to predict, with the desired precision, the(n+1)-th combination as a function of the first n combinations.

[0285] In the foregoing description, it was considered that the image iscomposed of points and that the processing operations of the describedmethods and systems are applied to points. Without departing from thescope of the invention, however, the described methods and systems couldprocess sets of points forming elements and representing patterns(lozenges, etc.).

[0286] In the case in which the appliance or the appliance chainpossesses a variable characteristic that may have only a reduced numberof discrete values (three discrete values of focal length, for example),it will be of interest, in terms of precision, to employ, according tothe adopted example, the process with fixed focal length three timesrather than to use an approximating polynomial surface that wouldinclude the focal length as parameter.

[0287] The field of application of the device can cover the field ofapplication related to image quality, its being understood that thequality of images can be measured in terms, among other factors, of theresidual distortion that they contain. The invention is also applicableto the art of measurement based on vision by computer, known by theexpression “vision metrology”.

[0288] Furthermore, the invention can be used to calculate the value ofthe focal length that was used to capture an image. In fact, startingfrom an image that is free of radial distortions because it has beencorrected, a person skilled in the art can use the geometric propertiesof vanishing points as described in the article of G.-Q. WEI et al.entitled “Camera Calibration by Vanishing Point and Cross Ratio”,published in IEEE International Conference on Acoustics Speech andSignal Processing, pages 1630-1633, Glasgow, Great Britain, May 1989.That will make it possible to obtain the focal distance of theimage-capture or image-restitution device as well as the position, onimage medium SC, of the intersection of the optical axis with thismedium. This information can be used, for example, in applications suchas vision metrology.

[0289] Furthermore, it will be noted that, with the exception of abilinear transformation, the knowledge of the universal set M isdefined, and that the image-capture and/or image-restitution device doesnot require any constraint of orthogonality at the filming moment. Thepositions of the points PT are not necessarily placed on regular shapes(line or circle), and may certainly have a random distribution.Furthermore, their relative position may be known with the mereexception of a scaling factor.

[0290] If the invention is employed in the case of an appliance chaincontaining a plurality of appliances, such as a projector and a photoappliance, or such as a printer and a scanner, and if one of theappliances, for example the photo appliance or the scanner, exhibitszero or little distortion defect, the method and system produceformatted information related solely to the other appliance. This is thecase of a practical method for producing formatted information relatedto an image-restitution appliance by using an image-capture appliancewhich is free of defects or whose defects have been measured andcorrected beforehand.

[0291] If the invention is employed in the case of an appliance chaincontaining a plurality of appliances, such as a photo appliance and ascanner, the method and system produce formatted information related toboth appliances. This is the case of a practical method for permittingthe correction of defects of a photo appliance without having to knowthe defects of the scanner, in the case in which the images used by thepresent method and system and by the image-processing means were scannedwith the same appliance.

Alternative Embodiment

[0292] Other characteristics and advantages of the invention will becomeapparent on reading:

[0293] of the definitions explained hereinafter of the employedtechnical terms, referring to the indicative and non-limitative examplesof FIGS. 1 to 12,

[0294] of the description of FIGS. 1 to 12.

Scene

[0295] Scene 3 is defined as a place in three-dimensional space,containing objects 107 illuminated by light sources.

Image-capture Appliance, Amage, Image Capture

[0296] Referring to FIGS. 3 and 7, a description will now be given ofwhat is understood by image-capture appliance 1 and image 103.Image-capture appliance 1 is defined as an appliance composed of anoptical system 100, of one or more sensors 101, of an electronic unit102 and of a memory zone 16. By means of the said image-captureappliance 1, it is possible to obtain, from a scene 3, fixed or animateddigital images 103 recorded in memory zone 16 or transmitted to anexternal device. Animated images are composed of a succession of fixedimages 103 in time. The said image-capture appliance 1 can have the formin particular of a photographic appliance, of a video camera, of acamera connected to or integrated in a PC, of a camera connected to orintegrated in a personal digital assistant, of a camera connected to orintegrated in a telephone, of a videoconferencing appliance or of ameasuring camera or appliance sensitive to wavelengths other than thoseof visible light, such as a thermal camera.

[0297] Image capture is defined as the method by which image 103 iscalculated by image-capture appliance 1.

[0298] In the case in which an appliance is equipped with a plurality ofinterchangeable subassemblies, especially an optical system 100,image-capture appliance 1 is defined as a special configuration of theappliance.

Image-restitution Means, Restituted Image, Image Restitution

[0299] Referring to FIG. 2, a description will now be given of what isunderstood by image-restitution means 19. Such an image-restitutionmeans 19 can have the form in particular of a visual display screen, ofa television screen, of a flat screen, of a projector, of virtualreality goggles, of a printer.

[0300] Such an image-restitution means 19 is composed of:

[0301] an electronic unit,

[0302] one or more sources of light, of electrons or of ink,

[0303] one or more modulators: devices for modulation of light, ofelectrons or of ink,

[0304] a focusing device, having in particular the form of an opticalsystem in the case of a light projector or the form of electron-beamfocusing coils in the case of a CRT screen, or the form of filters inthe case of a flat screen,

[0305] a restitution medium 190 having in particular the form of ascreen in the case of a CRT screen, of a flat screen or of a projector,the form of a print medium on which printing is performed in the case ofa printer, or the form of a virtual surface in space in the case of avirtual-image projector.

[0306] By means of the said image-restitution means 19, it is possibleto obtain, from an image 103, a restituted image 191 on restitutionmedium 190.

[0307] Animated images are composed of a succession of fixed images intime.

[0308] Image restitution is defined as the method by which the image isdisplayed or printed by means of image restitution means 19.

[0309] In the case in which a restitution means 19 is equipped with aplurality of interchangeable subassemblies or of subassemblies that canbe shifted relative to one another, especially restitution medium 190,image-restitution means 19 is defined as a special configuration.

Sensor Surface, Optical Center, Focal Distance

[0310] Referring to FIG. 1, a description will now be given of what isdefined as sensor surface 110.

[0311] Sensor surface 110 is defined as the shape in space drawn by thesensitive surface of sensor 101 of image-capture appliance 1 at themoment of image capture. This surface is generally plane.

[0312] An optical center 111 is defined as a point in space associatedwith image 103 at the moment of image capture. A focal distance isdefined as the distance between this point 111 and plane 110, in thecase in which sensor surface 110 is plane.

Pixel, Pixel Value, Exposure Time

[0313] Referring to FIG. 3, a description will now be given of what isunderstood by pixel 104 and pixel value.

[0314] A pixel 104 is defined as an elemental zone of sensor surface 110obtained by creating a grid, generally regular, of the said sensorsurface 110. Pixel value is defined as a number associated with thispixel 104.

[0315] Image capture is defined as determining the value of each pixel104. The set of these values constitutes image 103.

[0316] During image capture, the pixel value is obtained by integration,over the surface of pixel 104, during a time period defined as exposuretime, of part of the light flux derived from scene 3 via optical system100, and by converting the result of this integration to a digitalvalue. The integration of the light flux and/or the conversion of theresult of this integration to a digital value are performed by means ofelectronic unit 102.

[0317] This definition of the concept of pixel value is applicable tothe case of black-and-white or color images 103, whether they be fixedor animated.

[0318] Depending on the cases, however, the part in question of thelight flux is obtained in various ways:

[0319] a) In the case of a color image 103, sensor surface 110 isgenerally composed of a plurality of types of pixels 104, associatedrespectively with light fluxes of different wavelengths, examples beingred, green and blue pixels.

[0320] b) In the case of a color image 103, there may also be aplurality of sensors 101 disposed side-by-side, each receiving part ofthe light flux.

[0321] c) In the case of a color image 103, the colors used may bedifferent from red, green and blue, such as for North American NTSCtelevision, and they may exceed three in number.

[0322] d) Finally, in the case of an interlaced television scanningcamera, the animated images produced are composed of an alternation ofimages 103 containing even-numbered lines and of images 103 containingodd-numbered lines.

Configuration Used, Adjustments Used, Characteristics Used

[0323] The configuration used is defined as the list of removablesubassemblies of image-capture appliance 1, such as optical system 100which, if it is interchangeable, is mounted on image-capture appliance1. The configuration used is characterized in particular by:

[0324] the type of optical system 100,

[0325] the serial number of optical system 100 or any other designation.

[0326] Adjustments used are defined as:

[0327] the configuration used as defined hereinabove, as well as

[0328] the value of the manual or automatic adjustments available in theconfiguration used and having an impact on the content of image 103.These adjustments may be made by the user, especially by means ofpushbuttons, or may be calculated by image-capture appliance 1. Theseadjustments may be stored in the appliance, especially on a removablemedium, or on any device connected to the appliance. These adjustmentsmay include in particular the adjustments of focusing, diaphragm andfocal length of optical system 100, the adjustments of exposure time,the adjustments of white balance, and the integrated image-processingadjustments, such as digital zoom, compression and contrast.

[0329] Characteristics 74 used or set of characteristics 74 used aredefined as:

[0330] a) Parameters related to the intrinsic technical characteristicsof image-capture appliance 1, determined during the phase of design ofimage-capture appliance 1. For example, these parameters may include theformula of optical system 100 of the configuration used, which impactsthe geometric defects and the sharpness of the captured images; theformula of optical system 100 of the configuration used includes inparticular the shape, the arrangement and the material of the lenses ofoptical system 100.

[0331] These parameters may additionally include:

[0332] the geometry of sensor 101, or in other words sensor surface 110as well as the shape and relative arrangement of pixels 104 on thissurface,

[0333] the noise generated by electronic unit 102,

[0334] the equation for conversion of light flux to pixel value.

[0335] b) Parameters associated with the intrinsic technicalcharacteristics of image-capture appliance 1, determined during thephase of manufacture of image-capture appliance 1 and, in particular:

[0336] the exact positioning of the lenses in optical system 100 of theconfiguration used,

[0337] the exact positioning of optical system 100 relative to sensor101.

[0338] c) Parameters associated with the technical characteristics ofimage-capture appliance 1, determined at the moment of capture of image103 and, in particular:

[0339] the position and orientation of sensor surface 110 relative toscene 3,

[0340] the adjustments used,

[0341] the external factors, such as temperature, if they have aninfluence.

[0342] d) The user's preferences, especially the color temperature to beused for image restitution. For example, these preferences are selectedby the user by means of pushbuttons.

[0343] The characteristics 74 used include in particular the concept ofvariable characteristics.

Observation Point, Observation Direction

[0344] Referring to FIG. 1, a description will now be given of what isunderstood by observation point 105 and observation direction 106.

[0345] Mathematical surface 10 is defined as a surface that isgeometrically associated with sensor surface 110. For example, if thesensor surface is plane, it will be possible for mathematical surface 10to coincide with the sensor surface.

[0346] Observation direction 106 is defined as a line passing through atleast one point of scene 3 and through optical center 111. Observationpoint 105 is defined as the intersection of observation direction 106and surface 10.

Observed Color, Observed Intensity

[0347] Referring to FIG. 1, a description will now be given of what isunderstood by observed color and observed intensity. Observed color isdefined as the color of the light emitted, transmitted or reflected bythe said scene 3 in the said observation direction 106 at a giveninstant, and observed from the said observation point 105. Observedintensity is defined as the intensity of the light emitted by the saidscene 3 in the said observation direction 106 at the same instant, andobserved from the said observation point 105.

[0348] The color can be characterized in particular by a light intensitythat is a function of wavelength, or else by two values as measured by acalorimeter. The intensity can be characterized by a value such asmeasured with a photometer.

[0349] The said observed color and the said observed intensity depend inparticular on the relative position of objects 107 in scene 3 and on theillumination sources present as well as on the transparency andreflection characteristics of objects 107 at the moment of observation.

Mathematical Projection, Mathematical Image, Mathematical Point,Mathematical Color of a Point, Mathematical Intensity of a Point,Mathematical Shape of a Point, Mathematical Position of a Point

[0350] Generally speaking, a mathematical transformation such as amathematical projection is an operation making it possible to establisha correspondence between a first image and a second image and moreprecisely between a point of a first and a point of a second image.

[0351] In FIGS. 1 to 9 d, and in particular FIG. 5, a mathematicalprojection 8 has the purpose of constructing, from a real image or froma scene 3, a mathematical image 70 or, from a reference scene 9, asynthetic image.

[0352] In FIGS. 13a to 23 c, and in particular FIG. 14a, a mathematicalprojection H has the purpose of establishing a relationship between areal image (image 1 on FIG. 14a) and a virtual reference (R on FIG.14a), so as to establish the differences between the image and thevirtual reference, so as to have information for correcting the realimage.

[0353] Referring for example to FIGS. 1, 5, 9 a, 9 b, 9 c and 9 d, adescription in greater detail will be given of the concepts ofmathematical projection 8, mathematical image 70, mathematical point,mathematical color of a point, mathematical intensity of a point,mathematical shape 41 of a point, and mathematical position 40 of apoint.

[0354] Referring to FIG. 5, a description first of all will be given ofhow a mathematical image 70 is constructed by specified mathematicalprojection 8 of at least one scene 3 on mathematical surface 10.

[0355] Firstly, a description will be given of what is understood byspecified mathematical projection 8.

[0356] A specified mathematical projection 8 associates a mathematicalimage 70 with:

[0357] a scene 3 at the moment of capture of an image,

[0358] and with the characteristics 74 used.

[0359] A specified mathematical projection 8 is a transformation withwhich the characteristics of each point of mathematical image 70 can bedetermined from scene 3 at the moment of image capture and from thecharacteristics 74 used.

[0360] Mathematical projection 8 is preferentially defined in the mannerto be described hereinafter.

[0361] Mathematical position 40 of the point is defined as the positionof observation point 105 on mathematical surface 10.

[0362] Mathematical shape 41 of the point is defined as the geometric,punctiform shape of observation point 105.

[0363] Mathematical color of the point is defined as the observed color.

[0364] Mathematical intensity of the point is defined as the observedintensity.

[0365] Mathematical point is defined as the association of mathematicalposition 40, mathematical shape 41, mathematical color and mathematicalintensity for the observation point 105 under consideration.Mathematical image 70 is composed of the set of said mathematicalpoints.

[0366] The mathematical projection 8 of scene 3 is mathematical image70.

Real Projection, Real Point, Real Color of a Point, Real Intensity of aPoint, Real Shape of a Point, Real Position of a Point

[0367] Referring in particular to FIGS. 3, 5, 9 a, 9 b, 9 c and 9 d, adescription will be given hereinafter of the concepts of real projection72, real point, real color of a point, real intensity of a point, realshape 51 of a point, and real position 50 of a point.

[0368] During image capture, image-capture appliance 1 associated withthe characteristics 74 used, produces an image. In this way, on FIGS. 1and 7, there is obtained an image 103 of scene 3 and on FIGS. 13a and 14a, there is obtained an image I of universal set M. On FIG. 1, the lightoriginating from scene 3 in an observation direction 106 passes throughoptical system 100 and arrives at sensor surface 110.

[0369] For the said observation direction, there is then obtained whatis defined as a real point (or characteristic point PT on FIG. 3a),which exhibits differences compared with the mathematical point (orreference point PR on FIG. 32).

[0370] Referring to FIGS. 9a to 9 d, a description will now be given ofthe differences between the real point and the mathematical point.

[0371] The real shape 51 associated with the said observation direction106 is not a point on the sensor surface, but it has the form of a cloudin three-dimensional space, where it has an intersection with one ormore pixels 104. These differences are due in particular to coma,spherical aberration, astigmatism, grouping into pixels 104, chromaticaberration, depth of field, diffraction, parasitic reflections and fieldcurvature of image-capture appliance 1. They give an impression ofblurring, or of lack of sharpness of image 103.

[0372] In addition, real position 50 associated with the saidobservation direction 106 exhibits a difference compared withmathematical position 40 of a point. This difference is due inparticular to the geometric distortion, which gives an impression ofdeformation: for example, vertical walls appear to be curved. It is alsodue to the fact that the number of pixels 104 is limited, and thatconsequently the real position 50 can have only a finite number ofvalues.

[0373] In addition, the real intensity associated with the saidobservation direction 106 exhibits differences compared with themathematical intensity of a point. These differences are due inparticular to gamma and vignetting: for example, the edges of image 103appear to be darker. Furthermore, noise may be added to the signal.

[0374] Finally, the real color associated with the said observationdirection 106 exhibits differences compared with the mathematical colorof a point. These differences are due in particular to gamma and thecolor cast. Furthermore, noise may be added to the signal.

[0375] A real point is defined as the association of the real position50, the real shape 51, the real color and the real intensity for theobservation direction 106 under consideration.

[0376] The real projection 72 of scene 3 is composed of the set of realpoints.

Parameterizable Transformation Model, Parameters, Corrected Image

[0377] In an alternative embodiment, a parameterizable transformationmodel 12 (or parameterizable transformation 12 for short) is defined asa mathematical transformation in which a corrected image 71 can beobtained from an image 103 and from the value of parameters. Asindicated hereinbelow, the said parameters can in particular becalculated from the characteristics 74 used.

[0378] By means of the said parameterizable transformation 12, it ispossible in particular to determine, for each real point of image 103,the corrected position of the said real point, the corrected color ofthe said real point, the corrected intensity of the said real point, andthe corrected shape of the said real point, from the value of theparameters, from the real position of the said real point and from thevalues of the pixels of image 103. As an example, the corrected positioncan be calculated by means of polynomials of fixed degree as a functionof the real position, the coefficients of the polynomials depending onthe value of the parameters. The corrected color and the correctedintensity can be, for example, weighted sums of the values of thepixels, the coefficients depending on the value of the parameters and onthe real position, or else can be nonlinear functions of the values ofthe pixels of image 103.

[0379] The parameters can include in particular: the focal length ofoptical system 100 of the configuration used, or a related value such asthe position of a group of lenses, the focusing of optical system 100 ofthe configuration used, or a related value such as the position of agroup of lenses, the aperture of optical system 100 of the configurationused, or a related value such as the position of the diaphragm.

Difference Between the Mathematical Image and the Corrected Image

[0380] Referring to FIG. 5, the difference 73 between mathematical image70 and corrected image 71 for a given scene 3 and given characteristics74 used is defined as one or more values determined from numberscharacterizing the position, color, intensity, and shape of all or partof the corrected points and of all or part of the mathematical points.

[0381] For example, the difference 73 between mathematical image 70 andcorrected image 71 for a given scene 3 and given characteristics 74 usedcan be determined as follows:

[0382] There are chosen characteristic points which, for example, may bethe points of an orthogonal array 80 of regularly disposed points, asillustrated in FIG. 10.

[0383] The difference 73 is calculated, for example, by taking, for eachcharacteristic point, the sum of the absolute values of the differencesbetween each number characterizing position, color, intensity and shaperespectively for the corrected point and for the mathematical point. Thesum function of the absolute values of the differences may be replacedby another function such as the mean, the sum of the squares or anyother function with which the numbers can be combined.

Reference Scene or Universal Set

[0384] A reference scene 9 (or universal set M on FIGS. 13a andfollowing) is defined as a scene 3 for which certain characteristics areknown. As an example, FIG. 4a shows a reference scene 9 composed of apaper sheet bearing regularly disposed, solid black circles. FIG. 4bshows another paper sheet bearing the same circles, with the addition ofcolored lines and areas. The circles are used to measure the realposition 50 of a point, the lines to measure the real shape 51 of apoint, and the colored areas to measure the real color of a point andthe real intensity of a point. This reference scene 9 may be composed ofa material other than paper.

Reference Image

[0385] Referring to FIG. 12, a definition will now be given of theconcept of reference image 11 (or image I on the medium SC of FIGS. 13aand following). A reference image 11 is defined as an image of referencescene 9 obtained with image-capture appliance 1.

Synthetic Image, Synthetic-image Class

[0386] Referring to FIG. 12, a definition will now be given of theconcept of synthetic image and of synthetic-image class 7. A syntheticimage is defined as a mathematical image 70 obtained by mathematicalprojection 8 of a reference scene 9. A synthetic-image class 7 isdefined as a set of mathematical images 70 obtained by mathematicalprojection 8 of one or more reference scenes 9 for one or more sets ofcharacteristics 74 used. In the case in which there is only onereference scene 9 and only one set of characteristics 74 used, thesynthetic-image class 7 comprises only one synthetic image. On FIGS. 13aand following, the virtual reference R of the virtual surface SR can beconsidered as being such a synthetic image.

Transformed Image

[0387] Referring to FIG. 12, a definition will now be given of theconcept of transformed image 13. A transformed image 13 is defined asthe corrected image obtained by application of a parameterizabletransformation model 12 to a reference image 11.

Transformed Image Close to a Synthetic-image Class, Residual Deviation

[0388] Referring to FIG. 12, a description will now be given of theconcept of transformed image 13 close to a synthetic-image class 7 andof the concept of residual deviation 14.

[0389] The difference between a transformed image 13 and asynthetic-image class 7 is defined as the smallest difference betweenthe said transformed image 13 and any one of the synthetic images of thesaid synthetic-image class.

[0390] Referring to FIG. 12, a description will next be given of afourth algorithm with which it is possible to choose, among theparameterizable transformation models 12, that with which each referenceimage 11 can be transformed to a transformed image 13 close to thesynthetic-image class 7 of the reference scene 9 corresponding to thesaid reference image 11, in different cases of reference scenes 9 andcharacteristics 74 used.

[0391] In the case of a given reference scene 9 associated with a set ofgiven characteristics 74 used, there is chosen the parameterizabletransformation 12 (and its parameters) with which the reference image 11can be transformed to the transformed image 13 that exhibits thesmallest difference compared with synthetic-image class 7.Synthetic-image class 7 and transformed image 13 are then said to beclose. Residual deviation 14 is defined as the said difference.

[0392] In the case of a group of given reference scenes associated withsets of given characteristics 74 used, the parameterizabletransformation 12 (and its parameters) is chosen as a function of thedifferences between the transformed image 13 of each reference scene 9and the synthetic-image class 7 of each reference scene 9 underconsideration. There is chosen the parameterizable transformation 12(and its parameters) with which the reference images 11 can betransformed to transformed images 13 such that the sum of the saiddifferences is minimized. The sum function may be replaced by anotherfunction such as the product. Synthetic-image class 7 and transformedimages 13 are then said to be close. Residual deviation 14 is defined asa value obtained from the said differences, for example by calculatingthe mean thereof.

[0393] In the case in which certain characteristics 74 used are unknown,it is possible to determine them from the capture of a plurality ofreference images 11 of at least one reference scene 9. In this case,there are simultaneously determined the unknown characteristics and theparameterizable transformation 12 (and its parameters) with which thereference images 11 can be transformed to transformed images 13, suchthat the sum of the said differences is minimized, in particular byiterative calculation or by solving equations concerning the sum of thesaid differences and/or their product and/or any other appropriatecombination of the said differences. Synthetic-image class 7 andtransformed images 13 are then said to be close. The unknowncharacteristics may be, for example, the relative positions andorientations of sensor surface 110 and of each reference scene 9 underconsideration. Residual deviation 14 is defined as a value obtained fromthe said differences, for example by calculating the mean thereof.

Best Transformation

[0394] The best transformation is defined as the transformation withwhich, among the parameterizable transformation models 12, eachreference image 11 can be transformed to a transformed image 13 close tosynthetic-image class 7 of the reference scene 9 corresponding to thesaid reference image 11.

Calibration

[0395] Calibration is defined as a method with which data related to theintrinsic characteristics of image-capture appliance 1 can be obtained,for one or more configurations used, each composed of an optical system100 associated with an image-capture appliance 1.

[0396] Case 1: in the case in which there is only one configuration, thesaid method includes the following stages:

[0397] the stage of mounting the said optical system 100 on the saidimage-capture appliance 1,

[0398] the stage of choosing one or more reference scenes 9,

[0399] the stage of choosing several characteristics 74 used,

[0400] the stage of capturing images of the said reference scenes 9 forthe said characteristics used,

[0401] the stage of calculating the best transformation for each groupof reference scenes 9 corresponding to the same characteristics 74 used.

[0402] Case 2: in the case in which all the configurations correspondingto a given image-capture appliance 1 and to all optical systems 100 ofthe same type are taken into consideration, the said method includes thefollowing stages:

[0403] the stage of choosing one or more reference scenes 9,

[0404] the stage of choosing several characteristics 74 used,

[0405] the stage of calculating images 103 from characteristics 74 usedand in particular from formulas for optical system 100 of theconfiguration used and from values of parameters, by means, for example,of software for calculating the optical system by ray tracing,

[0406] the stage of calculating the best transformation for each groupof reference scenes 9 corresponding to the same characteristics used.

[0407] Case 3: in the case in which all the configurations correspondingto a given optical system 100 and to all the image-capture appliances 1of the same type are taken into consideration, the said method includesthe following stages:

[0408] the stage of mounting the said optical system 100 on animage-capture appliance 1 of the type under consideration,

[0409] the stage of choosing one or more reference scenes 9,

[0410] the stage of choosing several characteristics 74 used,

[0411] the stage of capturing images of the said reference scenes 9 forthe said characteristics used,

[0412] the stage of calculating the best transformation for each groupof reference scenes 9 corresponding to the same characteristics used.

[0413] Calibration can be performed preferentially by the manufacturerof image-capture appliance 1, for each appliance and configuration incase 1. This method is more precise but imposes more limitations and ishighly suitable in the case in which optical system 100 is notinterchangeable.

[0414] Alternatively, calibration can be performed by the manufacturerof image-capture appliance 1, for each appliance type and configurationin case 2. This method is less precise but is simpler.

[0415] Alternatively, calibration can be performed by the manufacturerof image-capture appliance 1 for each optical system 100 and type ofappliance in case 3. This method is a compromise in which one opticalsystem 100 can be used on all image-capture appliances 1 of one type,without repeating the calibration for each combination of image-captureappliance 1 and optical system 100.

[0416] Alternatively, calibration can be performed by the applianceseller or installer, for each image-capture appliance 1 andconfiguration in case 1.

[0417] Alternatively, calibration can be performed by the applianceseller or installer, for each optical system 100 and type of appliancein case 3.

[0418] Alternatively, calibration can be performed by the applianceuser, for each appliance and configuration in case 1.

[0419] Alternatively, calibration can be performed by the applianceuser, for each optical system 100 and type of appliance in case 3.

Design of the Digital Optical System

[0420] Design of the digital optical system is defined as a method forreducing the cost of optical system 100, by:

[0421] designing an optical system 100 having defects, especially inpositioning of real points, or choosing the same from a catalog,

[0422] reducing the number of lenses, and/or

[0423] simplifying the shape of the lenses, and/or

[0424] using less expensive materials, processing operations ormanufacturing processes.

[0425] The said method includes the following stages:

[0426] the stage of choosing an acceptable difference (within themeaning defined hereinabove),

[0427] the stage of choosing one or more reference scenes 9,

[0428] the stage of choosing several characteristics 74 used.

[0429] The said method also includes iteration of the following stages:

[0430] the stage of choosing an optical formula that includes inparticular the shape, material and arrangement of the lenses,

[0431] the stage of calculating images 103 from the characteristics 74used and in particular from the formulas for optical system 100 of theconfiguration used, by employing, for example, software for calculatingthe optical system by ray tracing, or by making measurements on aprototype,

[0432] the stage of calculating the best transformation for each groupof reference scenes 9 corresponding to the same characteristics 74 used,

[0433] the stage of verifying if the difference is acceptable, until thedifference is acceptable.

Formatted Information

[0434] Formatted information 15 associated with image 103, or formattedinformation 15, is defined as all or part of the following data:

[0435] data related to the intrinsic technical characteristics ofimage-capture appliance 1, especially the distortion characteristics,and/or

[0436] data related to the technical characteristics of image-captureappliance 1 at the moment of image capture, especially the exposuretime, and/or

[0437] data related to the preferences of the said user, especially thecolor temperature, and/or

[0438] data related to the residual deviations 14.

Database of Characteristics

[0439] A database 22 of characteristics is defined as a databasecontaining formatted information 15 for one or more image-captureappliances 1 and for one or more images 103.

[0440] The said database 22 of characteristics can be stored incentralized or distributed manner, and in particular can be:

[0441] integrated into image-capture appliance 1,

[0442] integrated into optical system 100,

[0443] integrated into a removable storage device,

[0444] integrated into a PC or other computer connected to the otherelements during image capture,

[0445] integrated into a PC or other computer connected to the otherelements after image capture,

[0446] integrated into a PC or other computer capable of reading astorage medium shared with image-capture appliance 1,

[0447] integrated into a remote server connected to a PC or othercomputer, itself connected to the other image-capture elements.

Fields

[0448] Referring to FIG. 8, a definition will now be given of theconcept of fields 90. The formatted information 15 associated with image103 can be recorded in several forms and structured into one or moretables, but it corresponds logically to all or part of fields 90,comprising:

[0449] (a) the focal distance,

[0450] (b) the depth of field

[0451] (c) the geometric defects.

[0452] The said geometric defects include geometric defects of image 103characterized by the parameters associated with the filmingcharacteristics 74 and a parameterizable transformation representing thecharacteristics of image-capture appliance 1 at the moment of filming.By means of the said parameters and of the said parameterizabletransformation, it is possible to calculate the corrected position of apoint of image 103.

[0453] The said geometric defects also include the vignettingcharacterized by the parameters associated with filming characteristics74 and a parameterizable transformation representing the characteristicsof image-capture appliance 1 at the moment of filming. By means of thesaid parameters and the said parameterizable transformation, it ispossible to calculate the corrected intensity of a point of image 103.

[0454] The said geometric defects also include the color castcharacterized by the parameters associated with filming characteristics74 and a parameterizable transformation representing the characteristicsof image-capture appliance 1 at the moment of filming. By means of thesaid parameters and the said parameterizable transformation, it ispossible to calculate the corrected color of a point of image 103.

[0455] The said fields 90 also include (d) the sharpness of image 103.

[0456] The said sharpness includes the blurring in resolution of image103 characterized by the parameters associated with filmingcharacteristics 74 and a parameterizable transformation representing thecharacteristics of image-capture appliance 1 at the moment of filming.By means of the said parameters and the said parameterizabletransformation, it is possible to calculate the corrected shape of apoint of image 103. Blurring covers in particular coma, sphericalaberration, astigmatism, grouping into pixels 104, chromatic aberration,depth of field, diffraction, parasitic reflections and field curvature.

[0457] The said sharpness also includes the blurring in depth of field,in particular spherical aberrations, coma and astigmatism. The saidblurring depends on the distance of the points of scene 3 relative toimage-capture appliance 1, and it is characterized by the parametersassociated with filming characteristics 74 and a parameterizabletransformation representing the characteristics of image-captureappliance 1 at the moment of filming. By means of the said parametersand of the said parameterizable transformation, it is possible tocalculate the corrected shape of a point of image 103.

[0458] The said fields 90 also include (e) parameters of thequantization method. The said parameters depend on the geometry andphysics of sensor 101, on the architecture of electronic unit 102 and onany processing software that may be used.

[0459] The said parameters include a function that represents thevariations of intensity of a pixel 104 as a function of wavelength andlight flux derived from the said scene 3. The said function includes inparticular gamma information.

[0460] The said parameters also include:

[0461] the geometry of the said sensor 101, especially the shape, therelative position and the number of sensitive elements of the saidsensor 101,

[0462] a function representative of the spatial and temporaldistribution of noise of image-capture appliance 1,

[0463] a value representative of the exposure time for image capture.

[0464] The said fields 90 also include (f) parameters of thedigital-processing operations performed by image-capture appliance 1,especially digital zoom and compression. These parameters depend on theprocessing software of image-capture appliance 1 and on the user'sadjustments.

[0465] The said fields 90 also include:

[0466] (g) parameters representative of the user's preferences,especially as regards the degree of blurring and the resolution of image103.

[0467] (h) the residual deviations 14.

Calculation of Formatted Information

[0468] The formatted information 15 can be calculated and recorded indatabase 22 in several stages.

[0469] a) A stage at the end of design of image-capture appliance 1.

[0470] By means of this stage it is possible to obtain intrinsictechnical characteristics of image-capture appliance 1, and inparticular:

[0471] the spatial and temporal distribution of the noise generated byelectronic unit 102,

[0472] the formula for conversion of light flux to pixel value,

[0473] the geometry of sensor 101.

[0474] b) A stage at the end of calibration or design of the digitaloptical system.

[0475] By means of this stage it is possible to obtain other intrinsictechnical characteristics of image-capture appliance 1, and inparticular, for a certain number of values of characteristics used, thebest associated transformation and the associated residual deviation 14.

[0476] c) A stage in which the user's preferences are chosen by means ofpushbuttons, menus or removable media, or of connection to anotherdevice.

[0477] d) An image capture stage.

[0478] By means of this stage (d) it is possible to obtain technicalcharacteristics of image-capture appliance 1 at the moment of imagecapture, and in particular the exposure time, which is determined by themanual or automatic adjustments made.

[0479] By means of stage (d) it is also possible to obtain the focaldistance. The focal distance is calculated from:

[0480] a measurement of the position of the group of lenses of variablefocal length of optical system 100 of the configuration used, or

[0481] a set value input to the positioning motor, or

[0482] a manufacturer's value if the focal length is fixed.

[0483] The said focal distance can then be determined by analysis of thecontent of image 103.

[0484] By means of stage (d) it is also possible to obtain the depth offield. The depth of field is calculated from:

[0485] a measurement of the position of the group of focusing lenses ofoptical system 100 of the configuration used, or

[0486] a set value input to the positioning motor, or

[0487] a manufacturer's value if the depth of field is fixed.

[0488] By means of stage (d) it is also possible to obtain the defectsof geometry and of sharpness. The defects of geometry and of sharpnesscorrespond to a transformation calculated by means of a combination oftransformations of the database 22 of characteristics obtained at theend of stage (b). This combination is chosen to represent the values ofparameters corresponding to the characteristics 74 used, especially thefocal distance.

[0489] By means of stage (d) it is also possible to obtain theparameters of digital processing performed by image-capture appliance 1.These parameters are determined by the manual or automatic adjustmentsmade.

[0490] The calculation of formatted information 15 according to stages(a) to (d) can be performed by:

[0491] a device or software integrated into image-capture appliance 1,and/or

[0492] driver software in a PC or other computer, and/or

[0493] software in a PC or other computer, and/or

[0494] a combination of the three.

[0495] The foregoing transformations in stage (b) and stage (d) can bestored in the form of:

[0496] a general mathematical formula,

[0497] a mathematical formula for each point,

[0498] a mathematical formula for certain characteristic points.

[0499] The mathematical formulas can be described by:

[0500] a list of coefficients,

[0501] a list of coefficients and coordinates.

[0502] By means of these different methods it is possible to reach acompromise between the size of the memory available for storage of theformulas and the calculating power available for calculation of thecorrected images 71.

[0503] In addition, in order to retrieve the data, identifiersassociated with the data are recorded in database 22. These identifiersinclude in particular:

[0504] an identifier of the type and of the reference of image-captureappliance 1,

[0505] an identifier of the type and of the reference of optical system100, if it is removable,

[0506] an identifier of the type and of the reference of any otherremovable element having a link to the stored information,

[0507] an identifier of image 103,

[0508] an identifier of the formatted information 15.

Completed Image

[0509] As described by FIG. 11, a completed image 120 is defined as theimage 103 associated with the formatted information 15. This completedimage 120 can preferentially have the form of a file. Completed image120 can also be distributed into a plurality of files.

[0510] Completed image 120 can be calculated by image-capture appliance1. It can also be calculated by an external calculating device, such asa computer.

Image-processing Software

[0511] Image-processing software 4 is defined as software that acceptsone or more completed images 120 as input and that performs processingoperations on these images. These processing operations can include inparticular:

[0512] calculating a corrected image 71,

[0513] performing measurements in the real world,

[0514] combining several images,

[0515] improving the fidelity of the images relative to the real world,

[0516] improving the subjective quality of images,

[0517] detecting objects or persons 107 in a scene 3,

[0518] adding objects or persons 107 to a scene 3,

[0519] replacing or modifying objects or persons 107 in a scene 3,

[0520] removing shadows from a scene 3,

[0521] adding shadows to a scene 3,

[0522] searching for objects in an image base.

[0523] The said image-processing software can be:

[0524] integrated into image-capture appliance 1,

[0525] run on calculating means 17 connected to image-capture appliance1 by transmission means 18.

Digital Optical System

[0526] A digital optical system is defined as the combination of animage-capture appliance 1, a database 22 of characteristics and acalculating means 17 that permits:

[0527] image capture of an image 103,

[0528] calculation of the completed image,

[0529] calculation of the corrected image 71.

[0530] Preferentially, the user obtains corrected image 71 directly. Ifhe wishes, the user may demand suppression of automatic correction.

[0531] The database 22 of characteristics may be:

[0532] integrated into image-capture appliance 1,

[0533] integrated into a PC or other computer connected to the otherelements during image capture,

[0534] integrated into a PC or other computer connected to the otherelements after image capture,

[0535] integrated into a PC or other computer capable of reading astorage medium shared with image-capture appliance 1,

[0536] integrated into a remote server connected to a PC or othercomputer, itself connected to the other image-capture elements.

[0537] Calculating means 17 may be:

[0538] integrated onto a component together with sensor 101,

[0539] integrated onto a component together with part of electronicsunit 102,

[0540] integrated into image-capture appliance 1,

[0541] integrated into a PC or other computer connected to the otherelements during image capture,

[0542] integrated into a PC or other computer connected to the otherelements after image capture,

[0543] integrated into a PC or other computer capable of reading astorage medium shared with image-capture appliance 1,

[0544] integrated into a remote server connected to a PC or othercomputer, itself connected to the other image-capture elements.

Processing of the Complete Chain

[0545] The foregoing paragraphs have essentially presented precisedetails of the concepts and description of the method and systemaccording to the invention for providing, to image-processing software4, formatted information 15 related to the characteristics ofimage-capture appliance 1.

[0546] In the paragraphs to follow, an expanded definition will be givenof the concepts and a supplemented description will be given of themethod and system according to the invention for providing, toimage-processing software 4, formatted information 15 related to thecharacteristics of image-restitution means 19. In this way theprocessing of a complete chain will be explained.

[0547] By means of the processing of the complete chain, it is possible:

[0548] to improve the quality of image 103 from one end of the chain tothe other, to obtain a restituted image 191 while correcting the defectsof image-capture appliance 1 and of image-restitution means 19, and/or

[0549] to use optical systems of lower quality and of lower cost in avideo projector in combination with software for improvement of imagequality.

Definitions Associated with the Image-restitution Means

[0550] On the basis of FIGS. 2 and 6, a description will now be given ofhow the characteristics of an image-restitution means 19 such as aprinter, a visual display screen or a projector are taken into accountin the formatted information 15.

[0551] The supplements or modifications to be made to the definitions inthe case of an image-restitution means 19 may be inferred by analogy bya person skilled in the art by analogy with the definitions provided inthe case of an image-capture appliance 1. Nevertheless, in order toillustrate this method, a description with reference in particular toFIG. 6 will now be given of the main supplements or modifications.

[0552] By restitution characteristics 95 used there are designated theintrinsic characteristics of image-restitution means 19, thecharacteristics of image-restitution means 19 at the moment of imagerestitution, and the user's preferences at the moment of imagerestitution. In the case of a projector in particular, the restitutioncharacteristics 95 used include the shape and position of the screenused. The concept of restitution characteristics 95 used is an extensionof the concept of variable characteristic.

[0553] By parameterizable restitution transformation model 97 (orparameterizable restitution transformation 97 for short), there isdesignated a mathematical transformation similar to parameterizabletransformation model 12.

[0554] By corrected restitution image 94 there is designated the imageobtained by application of parameterizable restitution transformation 97to image 103.

[0555] By mathematical restitution projection 96 there is designated amathematical projection that associates, with a corrected restitutionimage 94, a mathematical restitution image 92 on the mathematicalrestitution surface geometrically associated with the surface ofrestitution medium 190. The mathematical restitution points of themathematical restitution surface have a shape, position, color andintensity calculated from corrected restitution image 94.

[0556] By real restitution projection 90 there is designated aprojection that associates a restituted image 191 with an image 103. Thepixel values of image 103 are converted by the electronic unit ofrestitution means 19 to a signal that drives the modulator ofrestitution means 19. Real restitution points are obtained onrestitution medium 190. The said real restitution points arecharacterized by shape, color, intensity and position. The phenomenon ofgrouping into pixels 104 described hereinabove in the case of animage-capture appliance 1 does not occur in the case of animage-restitution means. On the other hand, an inverse phenomenonoccurs, with the result in particular that lines take on a staircaseappearance.

[0557] Restitution difference 93 is designated as the difference betweenrestituted image 191 and mathematical restitution image 92. Thisrestitution difference 93 is obtained by analogy with difference 73.

[0558] By restitution reference there is designated an image 103 inwhich the values of pixels 104 are known.

[0559] By best restitution transformation there is designated for arestitution reference and the restitution characteristics 95 used, thatwith which image 103 can be transformed to a corrected restitution image94 such that its mathematical restitution projection 92 exhibits theminimum restitution difference 93 compared with restituted image 191.

[0560] The methods of restitution calibration and of design of thedigital optical restitution system are comparable with the methods ofcalibration and of design of the digital optical system in the case ofan image-capture appliance 1. Nevertheless, differences are present incertain stages, and in particular the following stages:

[0561] the stage of choosing a restitution reference;

[0562] the stage of performing restitution of the said restitutionreference;

[0563] the stage of calculating the best restitution transformation.

[0564] The formatted information 15 related to an image-captureappliance 1 and that related to an image-restitution means 19 can beused end-to-end for the same image.

[0565] In the foregoing, a description was given of the concept of fieldin the case of an image-capture appliance 1. This concept is alsoapplicable by analogy in the case of image-restitution means 19.Nonetheless the parameters of the quantization method are replaced bythe parameters of the signal-reconstitution method, meaning: thegeometry of restitution medium 190 and its position, a functionrepresenting the spatial and temporal distribution of the noise ofimage-restitution means 19.

Generalization of the Concepts

[0566] The technical features of which the invention is composed andwhich are specified in the claims have been defined, described andillustrated by referring essentially to image-capture appliances ofdigital type, or in other words appliances that produce digital images.It can be easily understood that the same technical features areapplicable in the case of image-capture appliances that would be thecombination of an appliance based on silver technology (a photographicor cinematographic appliance using sensitive silver halide films,negatives or reversal films) with a scanner for producing a digitalimage from the developed sensitive films. Certainly it is appropriate inthis case to adapt at least some of the definitions used. Suchadaptations are within the capability of the person skilled in the art.In order to demonstrate the obvious character of such adaptations, it ismerely necessary to mention that the concepts of pixel and pixel valueillustrated by referring to FIG. 3 must, in the case of the combinationof an appliance based on silver technology with a scanner, be applied toan elemental zone of the surface of the film after this has beendigitized by means of the scanner. Such transpositions of definitionsare self-evident and can be extended to the concept of the configurationused. As an example, the list of removable subassemblies ofimage-capture appliance 1 included in the configuration used can besupplemented by the type of photographic film effectively used in theappliance based on silver technology.

Implementation of the System

[0567]FIG. 25 illustrates a practical example of the system with whichthe invention described in the foregoing can be employed. This systemcontains first calculating means MC1 related to an image I derived froman appliance APP1 and/or from an appliance chain P3 possessing variablecharacteristics. As described in the foregoing, these calculating meanswill calculate the measured formatted information IFM fromcharacteristics of the appliances, from variable characteristicsdepending on the image and from the associated values (focal length,focusing, speed, aperture, etc.). Second calculation means MC2 willcalculate the extended formatted information from the measured formattedinformation and from the variable characteristics and their associatedvalues, in such a way that the extended formatted information is morecompact in memory and makes it possible, as the case may be, to estimateinformation related to the distortion defect at points other than thepoints related to the measured formatted information. The measuredformatted information IFM and the extended formatted information IFE areprovided to selection means MS1 to produce formatted information IF.

Application of the Invention to Cost Reduction

[0568] Cost reduction is defined as a method and system for lowering thecost of an appliance or of an appliance chain P3, especially the cost ofthe optical system of an appliance or of an appliance chain, the methodconsisting in:

[0569] reducing the number of lenses, and/or

[0570] simplifying the shape of the lenses, and/or

[0571] designing an optical system having defects P5 that are largerthan those desired for the appliance or the appliance chain, or choosingthe same from a catalog, and/or

[0572] using materials, components, processing operations ormanufacturing methods that are less costly for the appliance or theappliance chain and that add defects P5.

[0573] The method and system according to the invention can be used tolower the cost of an appliance or of an appliance chain: it is possibleto design a digital optical system, to produce formatted information IFrelated to the defects of the appliance or of the appliance chain, touse this formatted information to enable image-processing means, whetherthey are integrated or not, to modify the quality of images derived fromor addressed to the appliance or to the appliance chain, in such a waythat the combination of the appliance or the appliance chain with theimage-processing means is capable of capturing, modifying or restitutingimages of the desired quality at reduced cost.

1-30. (Canceled). 31 A method for producing formatted informationrelated to appliances of an appliance chain, the appliance chainincluding at least one image-capture appliance and/or at least oneimage-restitution appliance, the method comprising: producing formattedinformation related to geometric distortions of at least one applianceof the appliance chain. 32 A method according to claim 31, wherein theappliance is configured to capture or restitute an image on a medium,the appliance containing at least one fixed characteristic and/or onevariable characteristic depending on the image, the fixed characteristicand/or variable characteristic configured to be associated with one ormore values of characteristics, and values of associatedcharacteristics; the method further comprising: producing, from ameasured field, measured formatted information related to geometricdistortions of the appliance, the formatted information configured toinclude the measured formatted information. 33 A method according toclaim 32, further comprising: producing extended formatted informationrelated to geometric distortions of the appliance from the measuredformatted information, the formatted information configured to includethe extended formatted information, the extended formatted informationexhibiting a deviation compared with the measured formatted information.34 A method according to claim 33, wherein the formatted informationproduced from the measured formatted information is represented byparameters of a parameterizable model chosen from among a set ofparameterizable models, the method further comprising: selecting theparameterizable model within the set of parameterizable models by:defining a maximum deviation, ordering the parameterizable models of theset of parameterizable models in accordance with their degree ofcomplexity of employment, choosing a first of the parameterizable modelsof the ordered set of parameterizable models such that the deviation issmaller than the maximum deviation. 35 A method according to claim 33,wherein the extended formatted information is the measured formattedinformation. 36 A method according to claim 33, including a firstcalculation algorithm with which the measured field can be obtained froma universal set containing characteristic points and from a virtualreference composed of reference points on a reference surface; the firstcalculation algorithm including capturing or restituting the universalset by the appliance to produce an image of the characteristic points onthe medium as a characteristic image point; the first calculationalgorithm further including: establishing a bijection between thecharacteristic image point and the reference points, selecting zero orone or more variable characteristics among the set of the variablecharacteristics; the measured field being composed of: the set of pairscomposed of one of the reference points and of the characteristic imagepoint associated by the bijection, and the value, for the image, of eachof the selected variable characteristics. 37 A method according to claim36, further comprising: choosing a mathematical projection, between themedium and the reference surface; wherein the measured field is composedof the value, for the image, of each of the selected variablecharacteristics and, for each reference point: of the pair composed ofthe reference point and of the mathematical projection, onto thereference surface, of the characteristic image point associated by thebijection with the reference point, and/or of the pair composed of thecharacteristic image point associated by the bijection with thereference point, and of the mathematical projection of the referencepoint onto the medium. 38 A method according to claim 36, furthercomprising: obtaining, from the measured formatted information, theextended formatted information related to an arbitrary reference pointon the reference surface and/or related to an arbitrary characteristicimage point of the medium, by deducing the formatted information relatedto the arbitrary reference point or to the arbitrary characteristicimage point. 39 A method according to claim 33, wherein the appliance ofthe appliance chain is provided with at least one variablecharacteristic depending on the image, each variable characteristicconfigured to be associated with a value to form a combination composedof the set of the variable characteristics and of the values; the methodfurther comprising: selecting predetermined combinations; andcalculating measured formatted information, including employing thefirst calculation algorithm for each of the selected predeterminedcombinations. 40 A method according to claim 39, wherein an argument isdefined, depending on a case, as: an arbitrary reference point on thereference surface and a combination, or an arbitrary characteristicimage point of the medium and a combination; the method furthercomprising: deducing, from the measured formatted information, theextended formatted information related to an arbitrary argument. 41 Amethod according to claim 38, wherein to deduce the extended formattedinformation from the measured formatted information: a first thresholdis defined, the extended formatted information is selected such that thedeviation is below the first threshold. 42 A method according to claim33, further comprising: associating the deviations with the formattedinformation. 43 A method according to claim 36, further comprising:selecting, on the medium, four characteristic image points such that aquadrilateral defined by the four characteristic image points has amaximum area and a center of gravity situated in proximity of thegeometric center of the image, the mathematical projection being abilinear transformation that transforms the four characteristic imagepoints to the reference points associated by bijection with the fourcharacteristic image points. 44 A method according to claim 36, whereinthe image is a color image composed of a plurality of color planes; themethod further comprising: producing the measured formatted informationby employing the first calculation algorithm for at least two of thecolor planes, by using a same mathematical projection for each of thecolor planes. 45 A method according to claim 36, wherein the image is acolor image composed of a plurality of color planes; the method furthercomprising: producing the measured formatted information by employingthe first calculation algorithm for at least one of the color planes, byusing a same virtual reference for each of the color planes; wherein theformatted information and/or measured formatted information can correctchromatic aberrations of the appliance. 46 A system for producingformatted information related to appliances of an appliance chain, theappliance chain including at least one image-capture appliance and/or atleast one image-restitution appliance, the system comprising:calculating means for producing formatted information related togeometric distortions of at least one appliance of the chain. 47 Asystem according to claim 46, wherein the appliance is configured tocapture or restitute an image on a medium, the appliance containing atleast one fixed characteristic and/or one variable characteristicdepending on the image, the fixed characteristic and/or variablecharacteristic configured to be associated with one or more values ofcharacteristics, and values of associated characteristics; the systemfurther comprising: calculating means for producing, from a measuredfield, measured formatted information related to the geometricdistortions of the appliance, the formatted information configured toinclude the measured formatted information. 48 A system according toclaim 47, further comprising: calculating means for producing extendedformatted information related to geometric distortions of the appliancefrom the measured formatted information, the formatted informationconfigured to include the extended formatted information, the extendedformatted information exhibiting a deviation compared with the measuredformatted information. 49 A system according to claim 48, wherein theformatted information produced from the measured formatted informationis represented by parameters of a parameterizable model chosen fromamong a set of parameterizable models; the system further comprising:selection means for selecting the parameterizable model within the setof parameterizable models, the selection means including data-processingmeans for: defining a maximum deviation, ordering the parameterizablemodels of the set of parameterizable models in accordance with theirdegree of complexity of employment, choosing a first of theparameterizable models of the ordered set of parameterizable models suchthat the deviation is smaller than the maximum deviation. 50 A systemaccording to claim 48, wherein the extended formatted information is themeasured formatted information. 51 A system according to claim 48,further comprising: calculating means employing a first calculationalgorithm with which the measured field can be obtained from a universalset containing characteristic points and from a virtual referencecomposed of reference points on a reference surface; the image-captureappliance or the image-restitution appliance including means forcapturing or means for restituting the universal set, with which meansan image of the characteristic points can be produced on the medium as acharacteristic image point; the means for calculation of the firstcalculation algorithm further including data-processing means for:establishing a bijection between the characteristic image point and thereference points, selecting zero or one or more variablecharacteristics, among the set of the variable characteristics; themeasured field being composed of: the set of pairs composed of one ofthe reference points and of the characteristic image point associated bythe bijection, and the value, for the image, of each of the selectedvariable characteristics. 52 A system according to claim 51, furthercomprising: analysis means for choosing a mathematical projection,between the medium and the reference surface; the measured field beingcomposed of the value, for the image, of each of the selected variablecharacteristics and, for each reference point: of the pair composed ofthe reference point and of the mathematical projection, onto thereference surface, of the characteristic image point associated by thebijection with the reference point, and/or of the pair composed of thecharacteristic image point associated by the bijection with thereference point, and of the mathematical projection of the referencepoint onto the medium. 53 A system according to claim 51, furthercomprising: data-processing means for obtaining, from the measuredformatted information, the extended formatted information related to anarbitrary reference point on the reference surface and/or related to anarbitrary characteristic image point of the medium, by deducing theformatted information related to the arbitrary reference point or to thearbitrary characteristic image point. 54 A system according to claim 48,wherein the appliance of the appliance chain is provided with at leastone variable characteristic depending on the image, each variablecharacteristic configured to be associated with a value to form acombination composed of the set of the variable characteristics and ofthe values; the system further comprising: selection means for selectingpredetermined combinations; and calculating means for calculatingmeasured formatted information, by employing the first calculationalgorithm for each of the selected predetermined combinations. 55 Asystem according to claim 54, wherein an argument designates, dependingon a case: an arbitrary reference point on the reference surface and acombination, or an arbitrary characteristic image point of the mediumand a combination; the system further comprising: data-processing meansfor deducing, from the measured formatted information, the extendedformatted information related to an arbitrary argument. 56 A systemaccording to claim 53, wherein to deduce the extended formattedinformation from the measured formatted information, the data-processingmeans includes selection means for selecting the extended formattedinformation such that the deviation is below a first threshold. 57 Asystem according to claim 48, wherein the deviations are associated withthe formatted information. 58 A system according to claim 51, furthercomprising: selection means for selecting, on the medium, fourcharacteristic image points such that a quadrilateral defined by thefour characteristic image points has a maximum area and a center ofgravity situated in proximity of a geometric center of the image, themathematical projection being a bilinear transformation that transformsthe four characteristic image points to the reference points associatedby the bijection with the four characteristic image points. 59 A systemaccording to claim 51, wherein the image is a color image composed of aplurality of color planes; the system further comprising:data-processing means for producing the measured formatted informationby employing the first calculation algorithm for at least two of thecolor planes, by using a same mathematical projection for each of thecolor planes; wherein the formatted information and/or measuredformatted information can correct distortions and/or chromaticaberrations of the appliance. 60 A system according to claim 51, whereinthe image is a color image composed of a plurality of color planes; thesystem further comprising: data-processing means for producing themeasured formatted information by employing the first calculationalgorithm for at least one of the color planes, by using a same virtualreference for each of the color planes; wherein the formattedinformation and/or measured formatted information can correct chromaticaberrations of the appliance.